Time Series EDA & Forecasting
# Find data from file for first combination
all_dates = data.frame(date=c("1/1/2013","2/1/2013","3/1/2013","4/1/2013","5/1/2013","6/1/2013","7/1/2013","8/1/2013","9/1/2013","10/1/2013","11/1/2013","12/1/2013",
"1/1/2014","2/1/2014","3/1/2014","4/1/2014","5/1/2014","6/1/2014","7/1/2014","8/1/2014","9/1/2014","10/1/2014","11/1/2014","12/1/2014",
"1/1/2015","2/1/2015","3/1/2015","4/1/2015","5/1/2015","6/1/2015","7/1/2015","8/1/2015","9/1/2015","10/1/2015","11/1/2015","12/1/2015",
"1/1/2016","2/1/2016","3/1/2016","4/1/2016","5/1/2016","6/1/2016","7/1/2016","8/1/2016","9/1/2016","10/1/2016","11/1/2016","12/1/2016",
"1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017",
"1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018",
"1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019"))
date_combinations = merge(all_dates,sample_combinations,all=TRUE)
str(date_combinations)
'data.frame': 840 obs. of 4 variables:
$ date : Factor w/ 84 levels "1/1/2013","1/1/2014",..: 1 29 36 43 50 57 64 71 78 8 ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 53 53 53 53 53 53 53 53 53 53 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 1144 1144 1144 1144 1144 1144 1144 1144 1144 1144 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
'data.frame': 840 obs. of 6 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 53 53 53 53 53 53 53 53 53 53 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 1144 1144 1144 1144 1144 1144 1144 1144 1144 1144 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
$ STD_Cases : num 3 0 1 5 0 0 0 4 0 0 ...
$ Dollar_Sales: num 582 0 206 970 0 0 0 814 0 0 ...
results <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
ljung_10=double(),
ljung_24=double(),
ljung_results=character(),
top_5_bic=character(),
ADF=double(),
PP=double(),
KPSS=double(),
stationarity_results=character(),
EqualMeans_1_ASE=double(),
EqualMeans_2_ASE=double(),
EqualMeans_3_ASE=double(),
EqualMeans_4_ASE=double(),
EqualMeans_5_ASE=double(),
EqualMeans_6_ASE=double(),
EqualMeans_7_ASE=double(),
EqualMeans_8_ASE=double(),
EqualMeans_9_ASE=double(),
EqualMeans_10_ASE=double(),
EqualMeans_11_ASE=double(),
EqualMeans_12_ASE=double(),
ARMA_1_ASE=double(),
ARMA_2_ASE=double(),
ARMA_3_ASE=double(),
ARMA_4_ASE=double(),
ARMA_5_ASE=double(),
ARMA_6_ASE=double(),
ARMA_7_ASE=double(),
ARMA_8_ASE=double(),
ARMA_9_ASE=double(),
ARMA_10_ASE=double(),
ARMA_11_ASE=double(),
ARMA_12_ASE=double(),
ARIMA_1_ASE=double(),
ARIMA_2_ASE=double(),
ARIMA_3_ASE=double(),
ARIMA_4_ASE=double(),
ARIMA_5_ASE=double(),
ARIMA_6_ASE=double(),
ARIMA_7_ASE=double(),
ARIMA_8_ASE=double(),
ARIMA_9_ASE=double(),
ARIMA_10_ASE=double(),
ARIMA_11_ASE=double(),
ARIMA_12_ASE=double(),
ARIMA_S12_1_ASE=double(),
ARIMA_S12_2_ASE=double(),
ARIMA_S12_3_ASE=double(),
ARIMA_S12_4_ASE=double(),
ARIMA_S12_5_ASE=double(),
ARIMA_S12_6_ASE=double(),
ARIMA_S12_7_ASE=double(),
ARIMA_S12_8_ASE=double(),
ARIMA_S12_9_ASE=double(),
ARIMA_S12_10_ASE=double(),
ARIMA_S12_11_ASE=double(),
ARIMA_S12_12_ASE=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:10) {
sample_combinations1 = sample_combinations[i,]
temp1 = inner_join(temp,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results[i,"Product_Type"] = product_type
results[i,"Product"] = as.character(sample_combinations1$Product)
results[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
par(mfrow = c(2,2))
invisible(acf(temp1$STD_Cases, main="ACF"))
invisible(parzen.wge(temp1$STD_Cases))
invisible(acf(temp1$STD_Cases[0:length(temp1$date)/2], main="ACF for 1st Half of Series"))
invisible(acf(temp1$STD_Cases[(1+length(temp1$date)/2):length(temp1$date)], main="ACF for 2nd Half of Series"))
sink("file")
ljung_10 = ljung.wge(temp1$STD_Cases,K=10)
sink()
cat("The Ljung-Box test with K=10 has a p-value of",ljung_10$pval,".")
results[i,"ljung_10"] = ljung_10$pval
sink("file")
ljung_24 = ljung.wge(temp1$STD_Cases,K=24)
sink()
cat("The Ljung-Box test with K=24 has a p-value of",ljung_24$pval,".")
results[i,"ljung_24"] = ljung_24$pval
if (ljung_10$pval < .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "not white noise"
} else if (ljung_10$pval > .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else if (ljung_10$pval < .05 & ljung_24$pval > .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else {
print("Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "white noise"
}
sink("file")
aic = invisible(aic5.wge(temp1$STD_Cases,type="bic"))
sink()
for (row in 1:nrow(aic)) {
if(aic[row,1] == 0 & aic[row,2] == 0){
print("One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise.")
results[i,"top_5_bic"] = "white noise"
}
}
# Tests for stationarity
# Augmented Dickey-Fuller
adf=tseries::adf.test(temp1$STD_Cases)
results[i,"ADF"] = adf$p.value
# Phillips-Perron
pp=tseries::pp.test(temp1$STD_Cases)
results[i,"PP"] = pp$p.value
# Kwiatkowski-Phillips-Schmidt-Shin
kpss=tseries::kpss.test(temp1$STD_Cases)
results[i,"KPSS"] = kpss$p.value
if (adf$p.value < .05 & kpss$p.value > .05){
print("Both stationarity tests indicate this time series is stationary.")
results[i,"stationarity_results"] = "stationary"
} else if (adf$p.value >= .05 & kpss$p.value <= .05){
print("Both stationarity tests indicate this time series is NOT stationary.")
results[i,"stationarity_results"] = "not stationary"
} else {
print("Both tests for stationarity were inconclusive.")
results[i,"stationarity_results"] = "inconclusive"
}
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
#ARMA Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARMA_1_ASE")] = WindowedASE1
results[i,paste0("ARMA_2_ASE")] = WindowedASE2
results[i,paste0("ARMA_3_ASE")] = WindowedASE3
results[i,paste0("ARMA_4_ASE")] = WindowedASE4
results[i,paste0("ARMA_5_ASE")] = WindowedASE5
results[i,paste0("ARMA_6_ASE")] = WindowedASE6
results[i,paste0("ARMA_7_ASE")] = WindowedASE7
results[i,paste0("ARMA_8_ASE")] = WindowedASE8
results[i,paste0("ARMA_9_ASE")] = WindowedASE9
results[i,paste0("ARMA_10_ASE")] = WindowedASE10
results[i,paste0("ARMA_11_ASE")] = WindowedASE11
results[i,paste0("ARMA_12_ASE")] = WindowedASE12
#ARIMA Model with d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_12_ASE")] = WindowedASE12
#ARIMA Model with S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-12))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMAS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_S12_12_ASE")] = WindowedASE12
#graph ASEs for each Model
EqualMeans_Results <- rbind(EqualMeans_Results_1,EqualMeans_Results_2,EqualMeans_Results_3,EqualMeans_Results_4,EqualMeans_Results_5,EqualMeans_Results_6,
EqualMeans_Results_7,EqualMeans_Results_8,EqualMeans_Results_9,EqualMeans_Results_10,EqualMeans_Results_11,EqualMeans_Results_12,
EqualMeans_Results_13)
ARMA_Results <- rbind(ARMA_Results_1,ARMA_Results_2,ARMA_Results_3,ARMA_Results_4,ARMA_Results_5,ARMA_Results_6,ARMA_Results_7,ARMA_Results_8,
ARMA_Results_9,ARMA_Results_10,ARMA_Results_11,ARMA_Results_12,ARMA_Results_13)
ARIMA_Results <- rbind(ARIMA_Results_1,ARIMA_Results_2,ARIMA_Results_3,ARIMA_Results_4,ARIMA_Results_5,ARIMA_Results_6,ARIMA_Results_7,ARIMA_Results_8,
ARIMA_Results_9,ARIMA_Results_10,ARIMA_Results_11,ARIMA_Results_12,ARIMA_Results_13)
ARIMAS_Results <- rbind(ARIMAS_Results_1,ARIMAS_Results_2,ARIMAS_Results_3,ARIMAS_Results_4,ARIMAS_Results_5,ARIMAS_Results_6,ARIMAS_Results_7,ARIMAS_Results_8,
ARIMAS_Results_9,ARIMAS_Results_10,ARIMAS_Results_11,ARIMAS_Results_12,ARIMAS_Results_13)
EqualMeans_Means <- colMeans(EqualMeans_Results)
ARMA_Means <- colMeans(ARMA_Results)
ARIMA_Means <- colMeans(ARIMA_Results)
ARIMAS_Means <- colMeans(ARIMAS_Results)
Combined_Means <- data.frame(EqualMeans_Means,ARMA_Means, ARIMA_Means, ARIMAS_Means)
Combined_Means$horizon <- as.numeric(row.names(Combined_Means))
g <- ggplot(data=Combined_Means, aes(horizon)) +
geom_line(aes(y=EqualMeans_Means, color="Equal Means"),size=1.5) +
geom_line(aes(y=ARMA_Means, color="ARMA"),size=1.5) +
geom_line(aes(y=ARIMA_Means, color="ARIMA"),size=1.5) +
geom_line(aes(y=ARIMAS_Means, color="ARIMAS"),size=1.5) +
scale_color_manual(values = c(
'Equal Means' = '#58508d',
'ARMA' = '#bc5090',
'ARIMA' = '#ff6361',
'ARIMAS' = '#ffa600'
)) +
labs(color='Models') +
scale_x_continuous(breaks=seq(0,13,1)) +
ggtitle(paste("Model ASEs for ", product,"and Customer",customer)) +
xlab("Month Ahead Forecast") +
ylab("ASE") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"), legend.title = element_blank())
print(g)
}

The Ljung-Box test with K=10 has a p-value of 4.786171e-12 .The Ljung-Box test with K=24 has a p-value of 1.665335e-15 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.08807377 .The Ljung-Box test with K=24 has a p-value of 0.3665865 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.3801776 .The Ljung-Box test with K=24 has a p-value of 0.1126704 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.0009489966 .The Ljung-Box test with K=24 has a p-value of 0.0002502709 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.0186233 .The Ljung-Box test with K=24 has a p-value of 0.005561031 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 6.426664e-08 .The Ljung-Box test with K=24 has a p-value of 6.568032e-10 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 2.934075e-10 .The Ljung-Box test with K=24 has a p-value of 3.874856e-11 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.6360152 .The Ljung-Box test with K=24 has a p-value of 0.844331 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0.06656046 .The Ljung-Box test with K=24 has a p-value of 0.0002436308 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.02684747 .The Ljung-Box test with K=24 has a p-value of 0.00508631 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."


|
Product_Type
|
Product
|
Customer
|
ljung_10
|
ljung_24
|
ljung_results
|
top_5_bic
|
ADF
|
PP
|
KPSS
|
stationarity_results
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
ARMA_1_ASE
|
ARMA_2_ASE
|
ARMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
ARIMA_1_ASE
|
ARIMA_2_ASE
|
ARIMA_3_ASE
|
ARIMA_4_ASE
|
ARIMA_5_ASE
|
ARIMA_6_ASE
|
ARIMA_7_ASE
|
ARIMA_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARIMA_11_ASE
|
ARIMA_12_ASE
|
ARIMA_S12_1_ASE
|
ARIMA_S12_2_ASE
|
ARIMA_S12_3_ASE
|
ARIMA_S12_4_ASE
|
ARIMA_S12_5_ASE
|
ARIMA_S12_6_ASE
|
ARIMA_S12_7_ASE
|
ARIMA_S12_8_ASE
|
ARIMA_S12_9_ASE
|
ARIMA_S12_10_ASE
|
ARIMA_S12_11_ASE
|
ARIMA_S12_12_ASE
|
|
700005895
|
JACK DANIELS BLK WHSKY 750M
|
700005895
|
4.786171e-12
|
1.665335e-15
|
not white noise
|
NA
|
0.01000000
|
0.01
|
0.01000000
|
inconclusive
|
9.0689744
|
9.1510256
|
9.1886325
|
9.1215385
|
9.2110256
|
9.6625641
|
9.9165934
|
10.1410897
|
10.2612821
|
10.3625641
|
10.2468298
|
10.6138462
|
5.5392257
|
5.9681069
|
6.3616927
|
6.4042988
|
6.4227636
|
6.4027409
|
6.6372491
|
6.7007490
|
6.7862477
|
6.7964345
|
6.7325565
|
6.8699152
|
8.5418681
|
9.2801912
|
8.6697046
|
9.3972474
|
9.5739507
|
9.0672141
|
9.6068875
|
9.4353998
|
9.5065170
|
9.6750977
|
9.3342680
|
9.3124904
|
9.155954
|
9.981319
|
10.046597
|
10.254467
|
10.473186
|
10.450372
|
10.530208
|
10.474697
|
10.512969
|
10.469637
|
10.440687
|
9.977978
|
|
700005448
|
TORTILLA SILVER TEQ DSS 1.75L
|
700005448
|
8.807377e-02
|
3.665865e-01
|
white noise
|
white noise
|
0.36870575
|
0.01
|
0.04762877
|
not stationary
|
0.5716556
|
0.5655017
|
0.5624248
|
0.5890915
|
0.6004761
|
0.6090060
|
0.6144394
|
0.6163350
|
0.6305729
|
0.6623222
|
0.6920285
|
0.7245188
|
0.7207688
|
0.6861532
|
0.6757951
|
0.6679848
|
0.6538848
|
0.6548755
|
0.6565445
|
0.6579988
|
0.6676573
|
0.6958380
|
0.7219311
|
0.7529246
|
0.6405925
|
0.6299260
|
0.6164646
|
0.6233314
|
0.6240543
|
0.6366802
|
0.6364920
|
0.6307053
|
0.6530211
|
0.6699885
|
0.6931830
|
0.7184782
|
2.032046
|
1.722801
|
1.651667
|
1.800139
|
2.099372
|
2.177397
|
2.211459
|
2.195750
|
2.284632
|
2.260631
|
2.206559
|
2.181600
|
|
701001904
|
FLOR DE CANA GOLD RUM 4YR 1L
|
701001904
|
3.801776e-01
|
1.126704e-01
|
white noise
|
white noise
|
0.01799216
|
0.01
|
0.01000000
|
inconclusive
|
0.6995513
|
0.5290385
|
0.4687821
|
0.4425000
|
0.4523718
|
0.4589530
|
0.4816026
|
0.5017949
|
0.5197792
|
0.5367308
|
0.5501340
|
0.5606624
|
1.0442915
|
0.8301104
|
0.7067074
|
0.6343535
|
0.6128546
|
0.5994945
|
0.5999598
|
0.6006017
|
0.6010513
|
0.6102566
|
0.6137591
|
0.6163443
|
1.9471137
|
1.3335527
|
1.1635218
|
1.0611714
|
1.0070012
|
1.0167857
|
0.9846006
|
1.0169535
|
1.0343591
|
1.0306570
|
1.0266886
|
1.0104301
|
1.772864
|
2.322617
|
2.302087
|
2.249213
|
2.201378
|
2.157702
|
2.153409
|
2.092362
|
2.041340
|
2.006400
|
1.978361
|
1.954473
|
|
700005925
|
CASA NOBLE CRYSTAL TEQ 6PK 750M
|
700005925
|
9.489966e-04
|
2.502709e-04
|
not white noise
|
white noise
|
0.56183917
|
0.01
|
0.10000000
|
inconclusive
|
3.1726068
|
3.2405556
|
2.7649145
|
2.5264530
|
2.3567094
|
2.2431197
|
2.1619841
|
2.0844658
|
2.0233191
|
1.9738889
|
1.9341453
|
1.8899145
|
4.6180807
|
4.3620508
|
3.4623764
|
3.1590933
|
2.8408239
|
2.6484569
|
2.5365775
|
2.3976202
|
2.3051311
|
2.2344015
|
2.1656202
|
2.1016788
|
5.7705099
|
5.0332742
|
4.0433952
|
3.6224390
|
3.1748973
|
3.0480544
|
2.8517410
|
2.6583122
|
2.6026693
|
2.4718678
|
2.3973343
|
2.3338023
|
6.311784
|
7.842069
|
8.196566
|
8.260550
|
8.124630
|
8.058478
|
7.944359
|
7.942456
|
7.808970
|
7.577955
|
7.185047
|
6.843732
|
|
701001770
|
PIRAS 51 CACHACA 80 1L
|
701001770
|
1.862330e-02
|
5.561031e-03
|
not white noise
|
white noise
|
0.21808191
|
0.01
|
0.07405623
|
inconclusive
|
0.7631624
|
0.7772650
|
0.7768376
|
0.7029060
|
0.6354701
|
0.6046154
|
0.6243346
|
0.6400855
|
0.6620228
|
0.6680342
|
0.6624631
|
0.6627350
|
0.6446346
|
0.6657276
|
0.6525417
|
0.5925254
|
0.5308812
|
0.5020173
|
0.5298103
|
0.5568825
|
0.5879643
|
0.6020638
|
0.6045947
|
0.6120855
|
0.5001610
|
0.5698775
|
0.6161735
|
0.6000020
|
0.5748791
|
0.5755548
|
0.6115162
|
0.6488012
|
0.6920893
|
0.7273038
|
0.7643915
|
0.7972260
|
2.133433
|
2.262730
|
2.385876
|
2.528330
|
2.554339
|
2.586967
|
2.650574
|
2.684445
|
2.731844
|
2.739532
|
2.727685
|
2.665071
|
|
701001908
|
MCCORMICK VODKA 80 1.75L
|
701001908
|
6.426664e-08
|
6.568032e-10
|
not white noise
|
NA
|
0.35666834
|
0.01
|
0.01740497
|
not stationary
|
2.2285256
|
2.4682692
|
2.0174145
|
1.8387821
|
1.7351923
|
1.6960470
|
1.6831044
|
1.6737179
|
1.6755342
|
1.6782692
|
1.6840035
|
1.6919872
|
2.6388001
|
2.9401760
|
2.8495561
|
2.5864904
|
2.3975025
|
2.3198086
|
2.2665143
|
2.2897757
|
2.3101477
|
2.2679359
|
2.1984358
|
2.1269990
|
2.8193328
|
3.1843995
|
3.1282574
|
3.0207945
|
2.9848679
|
3.0366704
|
2.9866854
|
2.8984773
|
2.8083638
|
2.6253766
|
2.4693641
|
2.3587800
|
2.718075
|
2.731890
|
2.603679
|
2.389337
|
2.253288
|
2.245274
|
2.215078
|
2.217877
|
2.233337
|
2.231591
|
2.235785
|
2.232764
|
|
700005926
|
MCCORMICK VODKA 80 TRVL 750M
|
700005926
|
2.934075e-10
|
3.874856e-11
|
not white noise
|
NA
|
0.07556121
|
0.01
|
0.10000000
|
inconclusive
|
0.6716239
|
0.5062393
|
0.4528205
|
0.4171368
|
0.3767521
|
0.3481197
|
0.3262027
|
0.3161752
|
0.3018234
|
0.2911111
|
0.2919037
|
0.2927778
|
0.5968163
|
0.4579625
|
0.4497181
|
0.4511265
|
0.4286348
|
0.4049576
|
0.3825204
|
0.3637018
|
0.3428458
|
0.3270631
|
0.3227980
|
0.3181758
|
0.7227470
|
0.5716485
|
0.6012171
|
0.6667377
|
0.7006763
|
0.6931936
|
0.7031823
|
0.7046800
|
0.6948582
|
0.6932306
|
0.6941748
|
0.6899631
|
2.242084
|
1.956935
|
2.049454
|
1.863884
|
1.727736
|
1.549117
|
1.558636
|
1.507911
|
1.542104
|
1.495994
|
1.398345
|
1.408520
|
|
701001850
|
RICH & RARE CANADIAN RSV 6PK 750M
|
701001850
|
6.360152e-01
|
8.443310e-01
|
white noise
|
white noise
|
0.03437467
|
0.01
|
0.10000000
|
stationary
|
11.9384353
|
10.4039481
|
9.9608284
|
10.6224737
|
10.8606404
|
11.0467686
|
11.1601569
|
11.2330186
|
11.2435663
|
11.3251840
|
11.5126614
|
11.6416917
|
10.9581620
|
10.7792516
|
11.0698208
|
12.0680550
|
12.0926064
|
12.3485626
|
12.1985500
|
12.0879789
|
12.0085145
|
12.0304265
|
12.1421225
|
12.2456236
|
15.9706679
|
14.2314006
|
12.0051347
|
14.2947521
|
14.6930887
|
14.8401812
|
14.7715397
|
15.1490428
|
14.9630577
|
15.3793237
|
15.4024611
|
15.4003271
|
15.974995
|
19.457766
|
19.705139
|
19.398040
|
17.880262
|
17.076971
|
16.550790
|
16.126107
|
15.784831
|
15.735607
|
15.589212
|
15.317253
|
|
700005900
|
1800 SILVER TEQ 750M
|
700005900
|
6.656046e-02
|
2.436308e-04
|
inconclusive
|
white noise
|
0.01000000
|
0.01
|
0.01000000
|
inconclusive
|
1.1171368
|
1.1107265
|
1.1102991
|
1.1081624
|
1.1017521
|
1.1201282
|
1.0706166
|
1.0338034
|
1.0060256
|
0.9840598
|
0.9670202
|
0.9756838
|
1.3180946
|
1.2112054
|
1.1772851
|
1.1584018
|
1.1419437
|
1.1536212
|
1.0993249
|
1.0589231
|
1.0283543
|
1.0041556
|
0.9852891
|
0.9924302
|
1.5081476
|
1.4503722
|
1.2940762
|
1.3000560
|
1.1782780
|
1.1161573
|
1.0581466
|
1.0076505
|
0.9827825
|
0.9540515
|
0.9299877
|
0.9175731
|
1.240651
|
1.421723
|
1.520543
|
1.556107
|
1.579133
|
1.557323
|
1.510084
|
1.475001
|
1.447865
|
1.426175
|
1.436418
|
1.425711
|
|
701001907
|
FIREBALL CINN WHSKY NL 1.75L
|
701001907
|
2.684747e-02
|
5.086310e-03
|
not white noise
|
white noise
|
0.04668965
|
0.01
|
0.01000000
|
inconclusive
|
3.4168803
|
3.3079060
|
2.9254274
|
2.7130342
|
2.5886752
|
2.5040598
|
2.4198107
|
2.3614316
|
2.3071937
|
2.2732906
|
2.2324981
|
2.2626068
|
3.7157148
|
3.4365654
|
3.1173311
|
2.9231418
|
2.8139887
|
2.7461842
|
2.6322659
|
2.5592011
|
2.4549392
|
2.3839704
|
2.3220255
|
2.3329799
|
5.8000703
|
5.1632613
|
4.7546005
|
4.5020618
|
4.4388272
|
4.0885904
|
3.9722412
|
3.9131948
|
3.7770898
|
3.7799643
|
3.7415046
|
3.8206163
|
3.409515
|
4.526630
|
5.475029
|
6.034526
|
5.773231
|
5.414569
|
5.320162
|
5.250294
|
5.147824
|
4.962384
|
4.771355
|
4.783543
|
Forecast Aggregation by Product
# Filter dataset to only 1 product
df_taaka = df[df$Product == "TAAKA VODKA 80 1L",]
combinations0 = as.data.frame(df_taaka %>% group_by(Product_Type, Product,Customer_ID) %>% tally(sort=TRUE))
combinations = combinations0 %>% filter(n >= 42)
combinations_mean = combinations0 %>% filter(n < 42)
# Find data from file for first combination
all_dates = data.frame(date=c("1/1/2013","2/1/2013","3/1/2013","4/1/2013","5/1/2013","6/1/2013","7/1/2013","8/1/2013","9/1/2013","10/1/2013","11/1/2013","12/1/2013",
"1/1/2014","2/1/2014","3/1/2014","4/1/2014","5/1/2014","6/1/2014","7/1/2014","8/1/2014","9/1/2014","10/1/2014","11/1/2014","12/1/2014",
"1/1/2015","2/1/2015","3/1/2015","4/1/2015","5/1/2015","6/1/2015","7/1/2015","8/1/2015","9/1/2015","10/1/2015","11/1/2015","12/1/2015",
"1/1/2016","2/1/2016","3/1/2016","4/1/2016","5/1/2016","6/1/2016","7/1/2016","8/1/2016","9/1/2016","10/1/2016","11/1/2016","12/1/2016",
"1/1/2017","2/1/2017","3/1/2017","4/1/2017","5/1/2017","6/1/2017","7/1/2017","8/1/2017","9/1/2017","10/1/2017","11/1/2017","12/1/2017",
"1/1/2018","2/1/2018","3/1/2018","4/1/2018","5/1/2018","6/1/2018","7/1/2018","8/1/2018","9/1/2018","10/1/2018","11/1/2018","12/1/2018",
"1/1/2019","2/1/2019","3/1/2019","4/1/2019","5/1/2019","6/1/2019","7/1/2019","8/1/2019","9/1/2019","10/1/2019","11/1/2019","12/1/2019"))
date_combinations = merge(all_dates,combinations,all=TRUE)
date_combinations_mean = merge(all_dates,combinations_mean,all=TRUE)
str(date_combinations)
'data.frame': 2436 obs. of 5 variables:
$ date : Factor w/ 84 levels "1/1/2013","1/1/2014",..: 1 29 36 43 50 57 64 71 78 8 ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 ...
$ n : int 84 84 84 84 84 84 84 84 84 84 ...
'data.frame': 2436 obs. of 7 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 700005925 ...
$ n : int 84 84 84 84 84 84 84 84 84 84 ...
$ STD_Cases : num 53.8 58 67 68 72 66 72 72 44 49.8 ...
$ Dollar_Sales: num 3135 3379 3903 3961 4194 ...
'data.frame': 336 obs. of 7 variables:
$ date : chr "1/1/2013" "2/1/2013" "3/1/2013" "4/1/2013" ...
$ Product_Type: Factor w/ 57 levels "ABSINTHE","AMARETTO",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Product : Factor w/ 4017 levels "1800 ANEJO TEQ 6PK 750M",..: 2325 2325 2325 2325 2325 2325 2325 2325 2325 2325 ...
$ Customer_ID : int 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 700005895 ...
$ n : int 39 39 39 39 39 39 39 39 39 39 ...
$ STD_Cases : num 2 0 0 1 1 0 0 0 0 0 ...
$ Dollar_Sales: num 117 0 0 58 58 0 0 0 0 0 ...
z = nrow(combinations)
results <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
ljung_10=double(),
ljung_24=double(),
ljung_results=character(),
top_5_bic=character(),
ADF=double(),
PP=double(),
KPSS=double(),
stationarity_results=character(),
EqualMeans_1_ASE=double(),
EqualMeans_2_ASE=double(),
EqualMeans_3_ASE=double(),
EqualMeans_4_ASE=double(),
EqualMeans_5_ASE=double(),
EqualMeans_6_ASE=double(),
EqualMeans_7_ASE=double(),
EqualMeans_8_ASE=double(),
EqualMeans_9_ASE=double(),
EqualMeans_10_ASE=double(),
EqualMeans_11_ASE=double(),
EqualMeans_12_ASE=double(),
EqualMeans_F1=double(),
EqualMeans_F2=double(),
EqualMeans_F3=double(),
EqualMeans_F4=double(),
EqualMeans_F5=double(),
EqualMeans_F6=double(),
EqualMeans_F7=double(),
EqualMeans_F8=double(),
EqualMeans_F9=double(),
EqualMeans_F10=double(),
EqualMeans_F11=double(),
EqualMeans_F12=double(),
ARMA_1_ASE=double(),
ARMA_2_ASE=double(),
ARMA_3_ASE=double(),
ARMA_4_ASE=double(),
ARMA_5_ASE=double(),
ARMA_6_ASE=double(),
ARMA_7_ASE=double(),
ARMA_8_ASE=double(),
ARMA_9_ASE=double(),
ARMA_10_ASE=double(),
ARMA_11_ASE=double(),
ARMA_12_ASE=double(),
ARMA_F1=double(),
ARMA_F2=double(),
ARMA_F3=double(),
ARMA_F4=double(),
ARMA_F5=double(),
ARMA_F6=double(),
ARMA_F7=double(),
ARMA_F8=double(),
ARMA_F9=double(),
ARMA_F10=double(),
ARMA_F11=double(),
ARMA_F12=double(),
ARIMA_1_ASE=double(),
ARIMA_2_ASE=double(),
ARIMA_3_ASE=double(),
ARIMA_4_ASE=double(),
ARIMA_5_ASE=double(),
ARIMA_6_ASE=double(),
ARIMA_7_ASE=double(),
ARIMA_8_ASE=double(),
ARIMA_9_ASE=double(),
ARIMA_10_ASE=double(),
ARIMA_11_ASE=double(),
ARIMA_12_ASE=double(),
ARIMA_F1=double(),
ARIMA_F2=double(),
ARIMA_F3=double(),
ARIMA_F4=double(),
ARIMA_F5=double(),
ARIMA_F6=double(),
ARIMA_F7=double(),
ARIMA_F8=double(),
ARIMA_F9=double(),
ARIMA_F10=double(),
ARIMA_F11=double(),
ARIMA_F12=double(),
ARIMA_S12_1_ASE=double(),
ARIMA_S12_2_ASE=double(),
ARIMA_S12_3_ASE=double(),
ARIMA_S12_4_ASE=double(),
ARIMA_S12_5_ASE=double(),
ARIMA_S12_6_ASE=double(),
ARIMA_S12_7_ASE=double(),
ARIMA_S12_8_ASE=double(),
ARIMA_S12_9_ASE=double(),
ARIMA_S12_10_ASE=double(),
ARIMA_S12_11_ASE=double(),
ARIMA_S12_12_ASE=double(),
ARIMA_S12_F1=double(),
ARIMA_S12_F2=double(),
ARIMA_S12_F3=double(),
ARIMA_S12_F4=double(),
ARIMA_S12_F5=double(),
ARIMA_S12_F6=double(),
ARIMA_S12_F7=double(),
ARIMA_S12_F8=double(),
ARIMA_S12_F9=double(),
ARIMA_S12_F10=double(),
ARIMA_S12_F11=double(),
ARIMA_S12_F12=double(),
ACTUAL_1=double(),
ACTUAL_2=double(),
ACTUAL_3=double(),
ACTUAL_4=double(),
ACTUAL_5=double(),
ACTUAL_6=double(),
ACTUAL_7=double(),
ACTUAL_8=double(),
ACTUAL_9=double(),
ACTUAL_10=double(),
ACTUAL_11=double(),
ACTUAL_12=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:z) {
sample_combinations1 = combinations[i,]
temp1 = inner_join(temp,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results[i,"Product_Type"] = product_type
results[i,"Product"] = as.character(sample_combinations1$Product)
results[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
par(mfrow = c(2,2))
invisible(acf(temp1$STD_Cases, main="ACF"))
invisible(parzen.wge(temp1$STD_Cases))
invisible(acf(temp1$STD_Cases[0:length(temp1$date)/2], main="ACF for 1st Half of Series"))
invisible(acf(temp1$STD_Cases[(1+length(temp1$date)/2):length(temp1$date)], main="ACF for 2nd Half of Series"))
sink("file")
ljung_10 = ljung.wge(temp1$STD_Cases,K=10)
sink()
cat("The Ljung-Box test with K=10 has a p-value of",ljung_10$pval,".")
results[i,"ljung_10"] = ljung_10$pval
sink("file")
ljung_24 = ljung.wge(temp1$STD_Cases,K=24)
sink()
cat("The Ljung-Box test with K=24 has a p-value of",ljung_24$pval,".")
results[i,"ljung_24"] = ljung_24$pval
if (ljung_10$pval < .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "not white noise"
} else if (ljung_10$pval > .05 & ljung_24$pval < .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else if (ljung_10$pval < .05 & ljung_24$pval > .05){
print("Ljung-Box test results: At a significance level of 0.05, the test is inconclusive.")
results[i,"ljung_results"] = "inconclusive"
} else {
print("Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise.")
results[i,"ljung_results"] = "white noise"
}
sink("file")
aic = invisible(aic5.wge(temp1$STD_Cases,type="bic"))
sink()
for (row in 1:nrow(aic)) {
if(aic[row,1] == 0 & aic[row,2] == 0){
print("One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise.")
results[i,"top_5_bic"] = "white noise"
}
}
# Tests for stationarity
# Augmented Dickey-Fuller
adf=tseries::adf.test(temp1$STD_Cases)
results[i,"ADF"] = adf$p.value
# Phillips-Perron
pp=tseries::pp.test(temp1$STD_Cases)
results[i,"PP"] = pp$p.value
# Kwiatkowski-Phillips-Schmidt-Shin
kpss=tseries::kpss.test(temp1$STD_Cases)
results[i,"KPSS"] = kpss$p.value
if (adf$p.value < .05 & kpss$p.value > .05){
print("Both stationarity tests indicate this time series is stationary.")
results[i,"stationarity_results"] = "stationary"
} else if (adf$p.value >= .05 & kpss$p.value <= .05){
print("Both stationarity tests indicate this time series is NOT stationary.")
results[i,"stationarity_results"] = "not stationary"
} else {
print("Both tests for stationarity were inconclusive.")
results[i,"stationarity_results"] = "inconclusive"
}
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
results[i,paste0("EqualMeans_F1")] = model0_mean
results[i,paste0("EqualMeans_F2")] = model0_mean
results[i,paste0("EqualMeans_F3")] = model0_mean
results[i,paste0("EqualMeans_F4")] = model0_mean
results[i,paste0("EqualMeans_F5")] = model0_mean
results[i,paste0("EqualMeans_F6")] = model0_mean
results[i,paste0("EqualMeans_F7")] = model0_mean
results[i,paste0("EqualMeans_F8")] = model0_mean
results[i,paste0("EqualMeans_F9")] = model0_mean
results[i,paste0("EqualMeans_F10")] = model0_mean
results[i,paste0("EqualMeans_F11")] = model0_mean
results[i,paste0("EqualMeans_F12")] = model0_mean
#ARMA Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARMA_1_ASE")] = WindowedASE1
results[i,paste0("ARMA_2_ASE")] = WindowedASE2
results[i,paste0("ARMA_3_ASE")] = WindowedASE3
results[i,paste0("ARMA_4_ASE")] = WindowedASE4
results[i,paste0("ARMA_5_ASE")] = WindowedASE5
results[i,paste0("ARMA_6_ASE")] = WindowedASE6
results[i,paste0("ARMA_7_ASE")] = WindowedASE7
results[i,paste0("ARMA_8_ASE")] = WindowedASE8
results[i,paste0("ARMA_9_ASE")] = WindowedASE9
results[i,paste0("ARMA_10_ASE")] = WindowedASE10
results[i,paste0("ARMA_11_ASE")] = WindowedASE11
results[i,paste0("ARMA_12_ASE")] = WindowedASE12
results[i,paste0("ARMA_F1")] = forecasts$f[1]
results[i,paste0("ARMA_F2")] = forecasts$f[2]
results[i,paste0("ARMA_F3")] = forecasts$f[3]
results[i,paste0("ARMA_F4")] = forecasts$f[4]
results[i,paste0("ARMA_F5")] = forecasts$f[5]
results[i,paste0("ARMA_F6")] = forecasts$f[6]
results[i,paste0("ARMA_F7")] = forecasts$f[7]
results[i,paste0("ARMA_F8")] = forecasts$f[8]
results[i,paste0("ARMA_F9")] = forecasts$f[9]
results[i,paste0("ARMA_F10")] = forecasts$f[10]
results[i,paste0("ARMA_F11")] = forecasts$f[11]
results[i,paste0("ARMA_F12")] = forecasts$f[12]
#ARIMA Model with d=1
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,1)
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-1))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-1))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 0, d = 1,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMA_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_F12")] = forecasts$f[12]
#ARIMA Model with S=12
nulldev()
temp2 = artrans.wge(temp1$STD_Cases,phi.tr=c(rep(0,11),1))
dev.off()
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model1 = invisible(aic.wge(temp2[k:(k+(trainingSize-1-12))],type="aic"))
model1_est = invisible(est.arma.wge(temp2[k:(k+(trainingSize-1-12))],p=model1$p,q=model1$q))
forecasts = fore.aruma.wge(temp1$STD_Cases[k:(k+(trainingSize-1))],phi = model1_est$phi, theta = model1_est$theta, s = 12, d = 0,n.ahead = j,plot=FALSE)
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - forecasts$f[1:1])^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - forecasts$f[1:2])^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - forecasts$f[1:3])^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - forecasts$f[1:4])^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - forecasts$f[1:5])^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - forecasts$f[1:6])^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - forecasts$f[1:7])^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - forecasts$f[1:8])^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - forecasts$f[1:9])^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - forecasts$f[1:10])^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - forecasts$f[1:11])^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - forecasts$f[1:12])^2)
sink()
assign(paste("ARIMAS_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - forecasts$f)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results[i,paste0("ARIMA_S12_1_ASE")] = WindowedASE1
results[i,paste0("ARIMA_S12_2_ASE")] = WindowedASE2
results[i,paste0("ARIMA_S12_3_ASE")] = WindowedASE3
results[i,paste0("ARIMA_S12_4_ASE")] = WindowedASE4
results[i,paste0("ARIMA_S12_5_ASE")] = WindowedASE5
results[i,paste0("ARIMA_S12_6_ASE")] = WindowedASE6
results[i,paste0("ARIMA_S12_7_ASE")] = WindowedASE7
results[i,paste0("ARIMA_S12_8_ASE")] = WindowedASE8
results[i,paste0("ARIMA_S12_9_ASE")] = WindowedASE9
results[i,paste0("ARIMA_S12_10_ASE")] = WindowedASE10
results[i,paste0("ARIMA_S12_11_ASE")] = WindowedASE11
results[i,paste0("ARIMA_S12_12_ASE")] = WindowedASE12
results[i,paste0("ARIMA_S12_F1")] = forecasts$f[1]
results[i,paste0("ARIMA_S12_F2")] = forecasts$f[2]
results[i,paste0("ARIMA_S12_F3")] = forecasts$f[3]
results[i,paste0("ARIMA_S12_F4")] = forecasts$f[4]
results[i,paste0("ARIMA_S12_F5")] = forecasts$f[5]
results[i,paste0("ARIMA_S12_F6")] = forecasts$f[6]
results[i,paste0("ARIMA_S12_F7")] = forecasts$f[7]
results[i,paste0("ARIMA_S12_F8")] = forecasts$f[8]
results[i,paste0("ARIMA_S12_F9")] = forecasts$f[9]
results[i,paste0("ARIMA_S12_F10")] = forecasts$f[10]
results[i,paste0("ARIMA_S12_F11")] = forecasts$f[11]
results[i,paste0("ARIMA_S12_F12")] = forecasts$f[12]
results[i,paste0("ACTUAL_1")] = temp1$STD_Cases[73]
results[i,paste0("ACTUAL_2")] = temp1$STD_Cases[74]
results[i,paste0("ACTUAL_3")] = temp1$STD_Cases[75]
results[i,paste0("ACTUAL_4")] = temp1$STD_Cases[76]
results[i,paste0("ACTUAL_5")] = temp1$STD_Cases[77]
results[i,paste0("ACTUAL_6")] = temp1$STD_Cases[78]
results[i,paste0("ACTUAL_7")] = temp1$STD_Cases[79]
results[i,paste0("ACTUAL_8")] = temp1$STD_Cases[80]
results[i,paste0("ACTUAL_9")] = temp1$STD_Cases[81]
results[i,paste0("ACTUAL_10")] = temp1$STD_Cases[82]
results[i,paste0("ACTUAL_11")] = temp1$STD_Cases[83]
results[i,paste0("ACTUAL_12")] = temp1$STD_Cases[84]
#graph ASEs for each Model
EqualMeans_Results <- rbind(EqualMeans_Results_1,EqualMeans_Results_2,EqualMeans_Results_3,EqualMeans_Results_4,EqualMeans_Results_5,EqualMeans_Results_6,
EqualMeans_Results_7,EqualMeans_Results_8,EqualMeans_Results_9,EqualMeans_Results_10,EqualMeans_Results_11,EqualMeans_Results_12,
EqualMeans_Results_13)
ARMA_Results <- rbind(ARMA_Results_1,ARMA_Results_2,ARMA_Results_3,ARMA_Results_4,ARMA_Results_5,ARMA_Results_6,ARMA_Results_7,ARMA_Results_8,
ARMA_Results_9,ARMA_Results_10,ARMA_Results_11,ARMA_Results_12,ARMA_Results_13)
ARIMA_Results <- rbind(ARIMA_Results_1,ARIMA_Results_2,ARIMA_Results_3,ARIMA_Results_4,ARIMA_Results_5,ARIMA_Results_6,ARIMA_Results_7,ARIMA_Results_8,
ARIMA_Results_9,ARIMA_Results_10,ARIMA_Results_11,ARIMA_Results_12,ARIMA_Results_13)
ARIMAS_Results <- rbind(ARIMAS_Results_1,ARIMAS_Results_2,ARIMAS_Results_3,ARIMAS_Results_4,ARIMAS_Results_5,ARIMAS_Results_6,ARIMAS_Results_7,ARIMAS_Results_8,
ARIMAS_Results_9,ARIMAS_Results_10,ARIMAS_Results_11,ARIMAS_Results_12,ARIMAS_Results_13)
EqualMeans_Means <- colMeans(EqualMeans_Results)
ARMA_Means <- colMeans(ARMA_Results)
ARIMA_Means <- colMeans(ARIMA_Results)
ARIMAS_Means <- colMeans(ARIMAS_Results)
Combined_Means <- data.frame(EqualMeans_Means,ARMA_Means, ARIMA_Means, ARIMAS_Means)
Combined_Means$horizon <- as.numeric(row.names(Combined_Means))
g <- ggplot(data=Combined_Means, aes(horizon)) +
geom_line(aes(y=EqualMeans_Means, color="Equal Means"),size=1.5) +
geom_line(aes(y=ARMA_Means, color="ARMA"),size=1.5) +
geom_line(aes(y=ARIMA_Means, color="ARIMA"),size=1.5) +
geom_line(aes(y=ARIMAS_Means, color="ARIMAS"),size=1.5) +
scale_color_manual(values = c(
'Equal Means' = '#58508d',
'ARMA' = '#bc5090',
'ARIMA' = '#ff6361',
'ARIMAS' = '#ffa600'
)) +
labs(color='Models') +
scale_x_continuous(breaks=seq(0,13,1)) +
ggtitle(paste("Model ASEs for ", product,"and Customer",customer)) +
xlab("Month Ahead Forecast") +
ylab("ASE") +
theme(panel.background = element_blank(), axis.line = element_line(colour = "black"), legend.title = element_blank())
print(g)
}

The Ljung-Box test with K=10 has a p-value of 0.002322024 .The Ljung-Box test with K=24 has a p-value of 0.0034262 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.007674533 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 1.18705e-12 .The Ljung-Box test with K=24 has a p-value of 1.573741e-12 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.006130321 .The Ljung-Box test with K=24 has a p-value of 0.03101856 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.2367016 .The Ljung-Box test with K=24 has a p-value of 0.122692 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 9.303002e-10 .The Ljung-Box test with K=24 has a p-value of 1.985955e-07 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.004701657 .The Ljung-Box test with K=24 has a p-value of 0.232411 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.02343884 .The Ljung-Box test with K=24 has a p-value of 0.1671774 .[1] "Ljung-Box test results: At a significance level of 0.05, the test is inconclusive."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.7619964 .The Ljung-Box test with K=24 has a p-value of 0.5395173 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 3.036074e-07 .The Ljung-Box test with K=24 has a p-value of 1.098736e-05 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1572929 .The Ljung-Box test with K=24 has a p-value of 0.3914269 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.3857241 .The Ljung-Box test with K=24 has a p-value of 0.7674317 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 2.798171e-05 .The Ljung-Box test with K=24 has a p-value of 0.0008768368 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 3.111778e-09 .The Ljung-Box test with K=24 has a p-value of 1.111885e-09 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 6.819545e-12 .The Ljung-Box test with K=24 has a p-value of 7.58651e-10 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 9.048454e-08 .The Ljung-Box test with K=24 has a p-value of 5.283449e-07 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.0009021729 .The Ljung-Box test with K=24 has a p-value of 0.01759268 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 1.110223e-16 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0.0739774 .The Ljung-Box test with K=24 has a p-value of 0.1020833 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1224362 .The Ljung-Box test with K=24 has a p-value of 0.4792812 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0 .The Ljung-Box test with K=24 has a p-value of 0 .[1] "Ljung-Box test results: At a significance level of 0.05, we reject the null hypothesis that this dataset is white noise."
[1] "Both tests for stationarity were inconclusive."



The Ljung-Box test with K=10 has a p-value of 0.1225766 .The Ljung-Box test with K=24 has a p-value of 0.5601297 .[1] "Ljung-Box test results: At a significance level of 0.05, we fail to reject the null hypothesis that this dataset is white noise."
[1] "One of the top 5 models using BIC was an ARMA(0,0), indicating this series may be white noise."
[1] "Both stationarity tests indicate this time series is NOT stationary."


vec=c("EqualMeans_12_ASE","ARMA_12_ASE","ARIMA_12_ASE","ARIMA_S12_12_ASE")
results$winning_12 <- colnames(results[c("EqualMeans_12_ASE","ARMA_12_ASE","ARIMA_12_ASE","ARIMA_S12_12_ASE")])[apply(results[c("EqualMeans_12_ASE","ARMA_12_ASE","ARIMA_12_ASE","ARIMA_S12_12_ASE")],1,which.min)]
formattable(results, align = c("l", rep("r", NCOL(table_a) - 1)))
|
Product_Type
|
Product
|
Customer
|
ljung_10
|
ljung_24
|
ljung_results
|
top_5_bic
|
ADF
|
PP
|
KPSS
|
stationarity_results
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
EqualMeans_F1
|
EqualMeans_F2
|
EqualMeans_F3
|
EqualMeans_F4
|
EqualMeans_F5
|
EqualMeans_F6
|
EqualMeans_F7
|
EqualMeans_F8
|
EqualMeans_F9
|
EqualMeans_F10
|
EqualMeans_F11
|
EqualMeans_F12
|
ARMA_1_ASE
|
ARMA_2_ASE
|
ARMA_3_ASE
|
ARMA_4_ASE
|
ARMA_5_ASE
|
ARMA_6_ASE
|
ARMA_7_ASE
|
ARMA_8_ASE
|
ARMA_9_ASE
|
ARMA_10_ASE
|
ARMA_11_ASE
|
ARMA_12_ASE
|
ARMA_F1
|
ARMA_F2
|
ARMA_F3
|
ARMA_F4
|
ARMA_F5
|
ARMA_F6
|
ARMA_F7
|
ARMA_F8
|
ARMA_F9
|
ARMA_F10
|
ARMA_F11
|
ARMA_F12
|
ARIMA_1_ASE
|
ARIMA_2_ASE
|
ARIMA_3_ASE
|
ARIMA_4_ASE
|
ARIMA_5_ASE
|
ARIMA_6_ASE
|
ARIMA_7_ASE
|
ARIMA_8_ASE
|
ARIMA_9_ASE
|
ARIMA_10_ASE
|
ARIMA_11_ASE
|
ARIMA_12_ASE
|
ARIMA_F1
|
ARIMA_F2
|
ARIMA_F3
|
ARIMA_F4
|
ARIMA_F5
|
ARIMA_F6
|
ARIMA_F7
|
ARIMA_F8
|
ARIMA_F9
|
ARIMA_F10
|
ARIMA_F11
|
ARIMA_F12
|
ARIMA_S12_1_ASE
|
ARIMA_S12_2_ASE
|
ARIMA_S12_3_ASE
|
ARIMA_S12_4_ASE
|
ARIMA_S12_5_ASE
|
ARIMA_S12_6_ASE
|
ARIMA_S12_7_ASE
|
ARIMA_S12_8_ASE
|
ARIMA_S12_9_ASE
|
ARIMA_S12_10_ASE
|
ARIMA_S12_11_ASE
|
ARIMA_S12_12_ASE
|
ARIMA_S12_F1
|
ARIMA_S12_F2
|
ARIMA_S12_F3
|
ARIMA_S12_F4
|
ARIMA_S12_F5
|
ARIMA_S12_F6
|
ARIMA_S12_F7
|
ARIMA_S12_F8
|
ARIMA_S12_F9
|
ARIMA_S12_F10
|
ARIMA_S12_F11
|
ARIMA_S12_F12
|
ACTUAL_1
|
ACTUAL_2
|
ACTUAL_3
|
ACTUAL_4
|
ACTUAL_5
|
ACTUAL_6
|
ACTUAL_7
|
ACTUAL_8
|
ACTUAL_9
|
ACTUAL_10
|
ACTUAL_11
|
ACTUAL_12
|
winning_12
|
|
700005925
|
TAAKA VODKA 80 1L
|
700005925
|
2.322024e-03
|
3.426200e-03
|
not white noise
|
white noise
|
0.16051598
|
0.01000000
|
0.08236679
|
inconclusive
|
819.7760806
|
812.0658241
|
815.1706105
|
689.8962088
|
601.4447370
|
535.9172216
|
489.1170549
|
453.3300453
|
418.8453911
|
390.5514370
|
364.9275910
|
346.4278690
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
51.2200000
|
1220.5831606
|
1064.5110370
|
1020.3968276
|
830.2558671
|
695.6295191
|
611.3732866
|
546.3263424
|
504.5024731
|
459.2102364
|
428.8023578
|
399.9887614
|
380.7868738
|
53.2174811
|
53.1162966
|
56.4704512
|
49.4069529
|
54.6932876
|
51.3134915
|
52.7815933
|
52.1860505
|
51.1159264
|
52.8831414
|
50.7506623
|
52.3671199
|
1.019355e+03
|
922.8713034
|
881.9550582
|
728.1959511
|
631.0542952
|
564.5865050
|
514.4789610
|
473.0086187
|
442.0050082
|
409.5462412
|
383.0871323
|
361.6927564
|
43.2661616
|
49.3228573
|
45.0704739
|
48.2247324
|
45.8328942
|
47.6618459
|
46.2589500
|
47.3362781
|
46.5086135
|
47.1445714
|
46.6558885
|
47.0314102
|
1057.5807655
|
1102.1703032
|
1124.8257798
|
1062.8228638
|
972.4991512
|
912.1410119
|
858.1841098
|
821.6447126
|
793.4708438
|
777.0434603
|
756.2249502
|
747.7752374
|
4.200000e+01
|
57.0000000
|
132.0000000
|
1.400000e+01
|
7.590000e+01
|
6.000000e+01
|
6.800000e+01
|
79.0000000
|
41.000000
|
70.00000000
|
54.000000
|
5.300000e+01
|
60
|
52.0
|
67
|
58
|
73
|
57
|
63.0
|
64.9
|
47.0
|
50
|
48
|
29.0
|
EqualMeans_12_ASE
|
|
701000317
|
TAAKA VODKA 80 1L
|
701000317
|
7.674533e-03
|
0.000000e+00
|
not white noise
|
white noise
|
0.01000000
|
0.01000000
|
0.10000000
|
stationary
|
5386.0238675
|
5396.0610470
|
6184.2691667
|
5551.7975855
|
5173.2864316
|
4930.6700214
|
4762.6696551
|
4625.7466239
|
4532.8720157
|
4471.5482265
|
4408.0742172
|
4359.0114744
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
101.7833333
|
5075.7624303
|
5322.5093370
|
5872.8687369
|
5059.3211299
|
4602.3842109
|
4469.0630501
|
4407.4131580
|
4192.1642408
|
4009.6761962
|
3890.3554742
|
3842.3155123
|
3826.2041051
|
95.1469980
|
111.2239452
|
130.6665597
|
134.9830893
|
132.6160184
|
120.0699014
|
107.4203227
|
94.9279002
|
87.1687948
|
84.0405840
|
86.5048168
|
92.1487011
|
1.067297e+04
|
8896.2364736
|
9681.2959002
|
7939.4196886
|
7844.4094248
|
7990.1810252
|
8554.5884303
|
8495.5687722
|
8648.9829702
|
8580.5019700
|
8400.6342571
|
8098.7569531
|
100.7928105
|
106.1108503
|
104.0874318
|
98.5400584
|
96.0526262
|
95.8392398
|
96.5603734
|
97.1166996
|
97.2837055
|
97.2210394
|
97.1236224
|
97.0748714
|
8433.3857923
|
5154.1237411
|
3763.1088122
|
2967.9427354
|
2477.4753201
|
2186.1939136
|
1971.8970771
|
1821.0071934
|
1703.7337292
|
1614.6880514
|
1527.2622155
|
1454.6370126
|
6.260246e+01
|
132.0000000
|
330.0000000
|
1.130000e+02
|
1.060000e+02
|
1.240000e+02
|
1.360000e+02
|
103.0000000
|
70.000000
|
25.00000000
|
47.000000
|
6.100000e+01
|
88
|
125.0
|
280
|
136
|
113
|
69
|
139.0
|
74.0
|
56.0
|
50
|
46
|
52.0
|
ARIMA_S12_12_ASE
|
|
701001770
|
TAAKA VODKA 80 1L
|
701001770
|
1.187050e-12
|
1.573741e-12
|
not white noise
|
NA
|
0.53188120
|
0.01000000
|
0.01000000
|
not stationary
|
6037.8142344
|
6011.6269267
|
6006.4802173
|
5164.7525036
|
4552.7806959
|
4119.4096190
|
3804.3090329
|
3566.8000677
|
3361.0108897
|
3202.4520857
|
3057.9253719
|
2878.2446917
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
106.4600000
|
6069.9661721
|
5828.9569556
|
5547.8444068
|
4610.1606213
|
3791.3806203
|
3434.4124271
|
3067.2728007
|
2758.3131160
|
2544.1935701
|
2414.8295759
|
2316.1141232
|
2178.9429165
|
126.1569793
|
146.0084226
|
143.7054915
|
138.8871720
|
139.8964381
|
139.0296243
|
137.7483457
|
137.0800734
|
136.2793895
|
135.4398483
|
134.6801564
|
133.9294398
|
5.254440e+03
|
4999.1257339
|
5154.8823639
|
4783.7655494
|
4074.9633382
|
3593.7634359
|
3216.2823825
|
2939.4821732
|
2754.5828734
|
2607.4836920
|
2519.5146881
|
2417.0835426
|
130.6083974
|
149.5099179
|
167.8357808
|
158.6284493
|
146.5277043
|
151.2585751
|
159.1897122
|
156.5399797
|
151.7689753
|
152.9420884
|
155.9654258
|
155.4518223
|
6366.8174714
|
6162.0168690
|
6965.4999529
|
6622.7382835
|
6228.5606941
|
6005.9191192
|
5770.7234801
|
5585.5098089
|
5469.6374123
|
5399.0224535
|
5393.4726932
|
5417.6119722
|
1.290000e+02
|
138.0000000
|
307.0000000
|
7.000000e+00
|
1.550000e+02
|
1.270000e+02
|
1.180000e+02
|
154.0000000
|
92.000000
|
148.00000000
|
192.000000
|
1.620000e+02
|
147
|
117.0
|
142
|
133
|
111
|
83
|
95.0
|
114.0
|
91.9
|
104
|
117
|
100.0
|
ARMA_12_ASE
|
|
701001790
|
TAAKA VODKA 80 1L
|
701001790
|
6.130321e-03
|
3.101856e-02
|
not white noise
|
NA
|
0.23862134
|
0.01000000
|
0.10000000
|
inconclusive
|
656.5598284
|
653.9191489
|
657.4544780
|
565.9303476
|
481.6666900
|
426.3206618
|
387.9143779
|
358.5476137
|
336.9679082
|
318.7005028
|
304.7056093
|
294.3995806
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
47.6116667
|
786.6821475
|
714.5797864
|
711.4097381
|
607.2602509
|
513.3486510
|
460.5636041
|
418.4143139
|
384.0363764
|
356.1047069
|
333.7082767
|
316.0337581
|
303.6017446
|
50.1138106
|
56.0235607
|
52.5582075
|
52.5577752
|
51.5403314
|
51.0307437
|
50.4769171
|
50.0500161
|
49.6736841
|
49.3599313
|
49.0923578
|
48.8662770
|
8.578247e+02
|
740.6271509
|
750.6761298
|
646.1743870
|
547.8952825
|
502.9643017
|
458.7333820
|
422.7623404
|
390.8696895
|
368.0898651
|
352.1400245
|
344.3114579
|
52.6895755
|
58.3425279
|
56.6876771
|
57.1721197
|
57.0303035
|
57.0718189
|
57.0596657
|
57.0632234
|
57.0621819
|
57.0624868
|
57.0623976
|
57.0624237
|
842.6917713
|
898.1286013
|
959.1236222
|
904.9257325
|
814.4104498
|
774.4453964
|
743.4284306
|
731.9260579
|
714.1077562
|
702.2044549
|
696.7813044
|
699.6595046
|
6.789311e+01
|
55.1070490
|
119.9267300
|
6.368655e+00
|
5.489771e+01
|
5.193155e+01
|
5.440356e+01
|
55.3706734
|
35.177312
|
54.14892463
|
59.076715
|
7.206030e+01
|
63
|
64.9
|
59
|
55
|
52
|
39
|
35.0
|
44.0
|
32.0
|
40
|
33
|
28.9
|
EqualMeans_12_ASE
|
|
700005448
|
TAAKA VODKA 80 1L
|
700005448
|
2.367016e-01
|
1.226920e-01
|
white noise
|
white noise
|
0.01000000
|
0.01000000
|
0.10000000
|
stationary
|
3.2606182
|
3.2721566
|
3.2434387
|
3.0333105
|
2.7925156
|
2.5995498
|
2.4645339
|
2.3359643
|
2.2364786
|
2.1533002
|
2.0775063
|
2.0241096
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
3.3155536
|
3.6951052
|
3.5728124
|
3.2893304
|
2.9938501
|
2.7727916
|
2.6125905
|
2.4612121
|
2.3474646
|
2.2538158
|
2.1690031
|
2.1081807
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
2.7483333
|
4.277557e+00
|
4.4170716
|
4.2540623
|
4.2082053
|
4.0010940
|
3.7620384
|
3.5477853
|
3.2918129
|
3.0336648
|
2.8995667
|
2.7235312
|
2.6498099
|
2.0911105
|
2.3396315
|
1.6856053
|
1.7126542
|
1.6028931
|
1.6049587
|
1.5864818
|
1.5864097
|
1.5832898
|
1.5832066
|
1.5826781
|
1.5826521
|
5.2717011
|
4.8712880
|
4.5640162
|
4.1850058
|
3.8107385
|
3.6754518
|
3.5445104
|
3.4088147
|
3.2588620
|
3.1409703
|
3.0572035
|
3.1602990
|
4.394872e+00
|
3.5629699
|
5.9933855
|
-6.026074e-01
|
8.879026e-01
|
3.114198e+00
|
9.120087e-01
|
3.1909435
|
2.023524
|
3.96196530
|
4.055599
|
5.935030e+00
|
3
|
4.0
|
3
|
2
|
3
|
2
|
1.9
|
2.0
|
2.0
|
3
|
2
|
1.0
|
EqualMeans_12_ASE
|
|
700005910
|
TAAKA VODKA 80 1L
|
700005910
|
9.303002e-10
|
1.985955e-07
|
not white noise
|
NA
|
0.41243058
|
0.01000000
|
0.01000000
|
not stationary
|
6.7172506
|
6.7250712
|
6.5282763
|
5.5460968
|
4.8189942
|
4.3354985
|
3.8306572
|
3.4450071
|
3.1375640
|
2.8975071
|
2.7124721
|
2.5533618
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
3.5983333
|
6.1013892
|
5.9203572
|
5.1397653
|
5.1470264
|
4.7179650
|
4.3067151
|
3.9056435
|
3.4903498
|
3.2598653
|
3.0606990
|
2.8924495
|
2.7913065
|
3.9108782
|
3.8589130
|
4.3650583
|
3.8193368
|
3.7658267
|
4.0203365
|
3.7464232
|
3.7050224
|
3.8320614
|
3.6942498
|
3.6657190
|
3.7285952
|
6.418067e+00
|
5.5822237
|
5.1910526
|
5.7611289
|
5.3900833
|
4.9376795
|
4.7755047
|
4.4630113
|
4.2442940
|
4.1784287
|
4.1535670
|
4.0361142
|
4.1969130
|
4.1858766
|
4.7215529
|
4.2985980
|
4.2868719
|
4.5737411
|
4.3510523
|
4.3417089
|
4.4952930
|
4.3780807
|
4.3714635
|
4.4536672
|
12.9033412
|
14.3702306
|
13.6094087
|
12.4329890
|
13.0086409
|
12.1494337
|
11.3515892
|
10.8322386
|
10.5849708
|
10.3442391
|
10.3014626
|
10.5715793
|
2.827593e+00
|
5.0647919
|
9.9032571
|
-2.211545e-01
|
3.837791e+00
|
7.005361e+00
|
1.897978e+00
|
5.0104979
|
2.971441
|
3.98968502
|
4.001390
|
4.989016e+00
|
3
|
4.0
|
3
|
3
|
3
|
3
|
3.0
|
3.0
|
2.0
|
3
|
2
|
4.0
|
EqualMeans_12_ASE
|
|
701001906
|
TAAKA VODKA 80 1L
|
701001906
|
4.701657e-03
|
2.324110e-01
|
inconclusive
|
white noise
|
0.25007229
|
0.01000000
|
0.05352414
|
inconclusive
|
65.2421581
|
65.6767735
|
65.4695085
|
59.6613889
|
55.7436966
|
53.1857479
|
51.1165171
|
49.5479274
|
47.2526994
|
45.3416453
|
42.9647688
|
40.5688675
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
8.1166667
|
85.6532674
|
78.3684495
|
78.3403061
|
75.3847626
|
70.1241569
|
67.8788493
|
64.9288995
|
62.8958034
|
60.2088286
|
57.8759763
|
55.0380537
|
51.8806305
|
14.4341932
|
14.2992145
|
14.2981344
|
13.8378853
|
13.5695530
|
13.3717741
|
13.1063023
|
12.8707520
|
12.6607333
|
12.4485251
|
12.2471629
|
12.0581166
|
7.502760e+01
|
72.4998836
|
66.2607202
|
62.0471258
|
56.6378845
|
53.7960147
|
51.3101253
|
50.7398992
|
50.4118597
|
51.3105130
|
50.7607084
|
49.5530079
|
16.8900802
|
14.4266741
|
16.4785885
|
15.8949595
|
15.5535072
|
16.2036223
|
15.5111407
|
15.9504079
|
15.9285781
|
15.6921148
|
15.9496297
|
15.7800948
|
70.6317652
|
70.6193852
|
84.1328390
|
86.1232897
|
87.6434001
|
87.6274218
|
86.3545689
|
89.4743434
|
92.0583899
|
95.7837302
|
98.3255336
|
99.7407462
|
1.378831e+01
|
16.7230399
|
31.2335027
|
6.236520e+00
|
1.494705e+01
|
1.903011e+01
|
1.666967e+01
|
23.7685567
|
15.962763
|
22.18950698
|
19.465098
|
2.001956e+01
|
5
|
10.0
|
8
|
12
|
11
|
7
|
5.0
|
4.0
|
4.0
|
7
|
5
|
10.0
|
EqualMeans_12_ASE
|
|
701001907
|
TAAKA VODKA 80 1L
|
701001907
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.42321723
|
0.01000000
|
0.01000000
|
not stationary
|
198.1483532
|
202.6365583
|
206.4005754
|
182.9596994
|
171.3135840
|
163.8738660
|
158.2928221
|
155.6318788
|
153.1012307
|
151.9063532
|
150.0285164
|
147.7516011
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
15.5316667
|
163.0706045
|
147.6514991
|
142.0327887
|
118.6811530
|
102.0842851
|
93.9061365
|
88.8143283
|
89.1300378
|
90.6734892
|
93.6587780
|
96.6549372
|
98.3048675
|
22.8958642
|
23.2461938
|
22.9222282
|
22.5535981
|
22.4030964
|
22.1708292
|
21.9334597
|
21.7335775
|
21.5290731
|
21.3288296
|
21.1394194
|
20.9548197
|
1.501983e+02
|
143.3290122
|
141.7302822
|
119.6492689
|
106.8935998
|
97.5921329
|
92.7298300
|
94.7295674
|
97.4315877
|
102.3187664
|
106.9094471
|
111.2762217
|
22.9247349
|
23.6428572
|
23.5853520
|
23.4369136
|
23.5165456
|
23.5172404
|
23.4976499
|
23.5061535
|
23.5071332
|
23.5046404
|
23.5055066
|
23.5057268
|
182.8480991
|
165.3978276
|
172.7950756
|
165.8161092
|
160.9457245
|
156.9136823
|
154.9305126
|
158.5704392
|
161.8352075
|
168.1169377
|
174.1008125
|
178.8359459
|
1.700000e+01
|
21.0000000
|
50.0000000
|
1.000000e+00
|
2.200000e+01
|
2.500000e+01
|
1.900000e+01
|
27.0000000
|
19.000000
|
27.00000000
|
25.000000
|
2.400000e+01
|
28
|
25.0
|
26
|
23
|
30
|
22
|
7.0
|
4.0
|
5.0
|
5
|
8
|
21.0
|
ARMA_12_ASE
|
|
700005900
|
TAAKA VODKA 80 1L
|
700005900
|
2.343884e-02
|
1.671774e-01
|
inconclusive
|
white noise
|
0.18041707
|
0.01000000
|
0.04977124
|
not stationary
|
1.1002137
|
1.2694444
|
1.3378205
|
1.4354701
|
1.5084188
|
1.5634615
|
1.6097375
|
1.6434829
|
1.6663105
|
1.6832906
|
1.7279526
|
1.7576923
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.0595516
|
1.3538109
|
1.4839001
|
1.5426432
|
1.5814597
|
1.6259222
|
1.6660457
|
1.6941691
|
1.7063527
|
1.7177220
|
1.7584874
|
1.7871770
|
2.5412846
|
2.2836974
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.8833333
|
1.101310e+00
|
1.3801037
|
1.3854369
|
1.5228097
|
1.5577902
|
1.5402714
|
1.5416878
|
1.5752095
|
1.5914634
|
1.6536299
|
1.7103440
|
1.7389881
|
1.2338905
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.0000244
|
1.1350347
|
1.5327785
|
1.7377441
|
2.0966865
|
2.4001221
|
2.5704244
|
2.7177394
|
2.8033499
|
2.8996504
|
2.9787477
|
3.0430483
|
3.0968104
|
3.179249e+00
|
1.6499882
|
3.0000000
|
0.000000e+00
|
1.000000e+00
|
2.000000e+00
|
3.000000e+00
|
3.0000000
|
3.000000
|
2.00000000
|
3.000000
|
3.000000e+00
|
3
|
4.0
|
3
|
4
|
4
|
3
|
1.0
|
3.0
|
1.0
|
3
|
4
|
2.0
|
ARIMA_12_ASE
|
|
701001768
|
TAAKA VODKA 80 1L
|
701001768
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.48537100
|
0.01000000
|
0.10000000
|
inconclusive
|
4.8252590
|
4.7309000
|
4.6891906
|
4.6548744
|
4.4625923
|
4.1009000
|
3.8908267
|
3.6955154
|
3.6434926
|
3.5806949
|
3.4830212
|
3.3908573
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
4.7800000
|
5.2605927
|
5.2971241
|
5.2878247
|
5.1661669
|
4.8449283
|
4.4788421
|
4.1976426
|
3.9212539
|
3.8133806
|
3.6862767
|
3.5379249
|
3.3946633
|
5.2292753
|
5.1602537
|
5.1018358
|
5.0523925
|
5.0105452
|
4.9751268
|
4.9451497
|
4.9197779
|
4.8983040
|
4.8801291
|
4.8647464
|
4.8517269
|
5.732139e+00
|
5.7316454
|
5.9547820
|
5.9448081
|
5.6364189
|
5.3917974
|
5.0619394
|
4.6936530
|
4.5424800
|
4.3541588
|
4.1646626
|
3.9649667
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
5.0704608
|
4.9079529
|
4.9796347
|
5.0235500
|
5.3625069
|
5.1204962
|
4.9727518
|
4.8140382
|
4.7272246
|
4.7013188
|
4.6820765
|
4.7368283
|
4.7764556
|
4.750496e+00
|
5.5412219
|
6.3569667
|
9.194740e+00
|
1.051907e+00
|
3.926150e+00
|
2.815428e+00
|
3.7179421
|
3.632111
|
2.55654143
|
6.490006
|
6.431425e+00
|
4
|
5.0
|
4
|
5
|
4
|
3
|
2.0
|
3.0
|
1.0
|
5
|
4
|
4.0
|
EqualMeans_12_ASE
|
|
700005850
|
TAAKA VODKA 80 1L
|
700005850
|
7.619964e-01
|
5.395173e-01
|
white noise
|
white noise
|
0.05366716
|
0.01000000
|
0.10000000
|
inconclusive
|
0.7946959
|
0.8499524
|
0.8584566
|
0.8650806
|
0.8504908
|
0.8458498
|
0.8291648
|
0.8163306
|
0.8088270
|
0.8013113
|
0.7960479
|
0.7916831
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
0.9313236
|
0.9150902
|
0.8734208
|
0.8846560
|
0.8540705
|
0.8458284
|
0.8180487
|
0.7992410
|
0.8014471
|
0.7953921
|
0.7858098
|
0.7843329
|
1.1990399
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.6633333
|
1.668124e+00
|
1.7099588
|
1.4339802
|
1.4716291
|
1.4078535
|
1.4279774
|
1.3607470
|
1.3316285
|
1.2889480
|
1.2815764
|
1.2452635
|
1.2593410
|
-0.2756279
|
0.6273774
|
0.3784424
|
0.4470673
|
0.4281492
|
0.4333644
|
0.4319267
|
0.4323230
|
0.4322138
|
0.4322439
|
0.4322356
|
0.4322379
|
2.2656312
|
2.5005957
|
2.5645920
|
2.5893030
|
2.5160865
|
2.4683782
|
2.4140848
|
2.3876108
|
2.3643841
|
2.3484751
|
2.3305555
|
2.3184846
|
1.250166e+00
|
1.0000000
|
3.0000000
|
0.000000e+00
|
2.000000e+00
|
3.000000e+00
|
2.000000e+00
|
2.0000000
|
1.000000
|
2.00000000
|
2.000000
|
3.000000e+00
|
1
|
3.0
|
2
|
3
|
3
|
1
|
1.0
|
2.0
|
1.0
|
1
|
1
|
2.0
|
ARMA_12_ASE
|
|
701001830
|
TAAKA VODKA 80 1L
|
701001830
|
3.036074e-07
|
1.098736e-05
|
not white noise
|
NA
|
0.01094855
|
0.01000000
|
0.01000000
|
inconclusive
|
3.3812607
|
3.3274145
|
3.5419444
|
3.2107479
|
2.9817735
|
2.8650214
|
2.8658761
|
2.8812607
|
2.8883832
|
2.8969017
|
2.9194891
|
2.9395940
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
4.0075461
|
3.4573960
|
3.5632091
|
3.2119952
|
2.9633191
|
2.8283792
|
2.8212973
|
2.8343839
|
2.8403766
|
2.8490921
|
2.8750325
|
2.8932145
|
2.6117949
|
2.5657214
|
2.5242446
|
2.4869061
|
2.4532928
|
2.4230331
|
2.3957925
|
2.3712696
|
2.3491934
|
2.3293198
|
2.3114290
|
2.2953231
|
5.693141e+00
|
5.0198774
|
5.5617559
|
5.3414683
|
5.1786422
|
5.1347258
|
5.2635862
|
5.2865471
|
5.2943238
|
5.2846495
|
5.1757291
|
5.0886369
|
1.3356365
|
1.5349088
|
1.2718390
|
1.1519197
|
1.0750150
|
1.0476987
|
1.0247556
|
1.0139917
|
1.0077459
|
1.0043690
|
1.0024185
|
1.0013730
|
4.7621704
|
4.6118544
|
4.6386722
|
4.2674657
|
4.0447418
|
3.8577976
|
3.8231672
|
3.7875790
|
3.7342582
|
3.6916017
|
3.6776798
|
3.6660783
|
4.000000e+00
|
2.0000000
|
7.0000000
|
0.000000e+00
|
3.000000e+00
|
3.000000e+00
|
2.000000e+00
|
3.0000000
|
3.000000
|
2.00000000
|
3.000000
|
4.000000e+00
|
3
|
4.0
|
5
|
4
|
3
|
4
|
5.0
|
3.0
|
2.0
|
3
|
1
|
3.0
|
ARMA_12_ASE
|
|
701001767
|
TAAKA VODKA 80 1L
|
701001767
|
1.572929e-01
|
3.914269e-01
|
white noise
|
white noise
|
0.34274666
|
0.01000000
|
0.10000000
|
inconclusive
|
1.3459188
|
1.3023291
|
1.2066026
|
1.0946368
|
1.0100214
|
0.9544658
|
0.9140507
|
0.8667521
|
0.8387963
|
0.8400214
|
0.8181799
|
0.7995513
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
2.2166667
|
1.9333380
|
1.6235733
|
1.4734965
|
1.3692537
|
1.2889112
|
1.1850200
|
1.1032948
|
1.0376552
|
0.9962394
|
0.9859819
|
0.9518440
|
0.9239290
|
2.3807007
|
2.1586414
|
2.9171391
|
2.5688481
|
2.2957120
|
2.3980851
|
2.2483458
|
2.5293585
|
2.3935057
|
2.3247667
|
2.3487997
|
2.2723652
|
1.750115e+00
|
1.6015794
|
1.4961143
|
1.4375831
|
1.3589958
|
1.2541742
|
1.1713232
|
1.1220013
|
1.0900889
|
1.0678670
|
1.0191809
|
0.9856034
|
2.4672700
|
2.3263118
|
3.1237031
|
2.7179263
|
2.4276452
|
2.5866173
|
2.4886757
|
2.8069577
|
2.6373311
|
2.5623943
|
2.6135525
|
2.5630514
|
3.5248317
|
3.1975521
|
3.0058058
|
2.8039407
|
2.5891514
|
2.4424681
|
2.3937548
|
2.2516280
|
2.1680245
|
2.1171107
|
2.0662856
|
2.0406461
|
1.388334e+00
|
-0.1222036
|
4.3714579
|
9.221621e-01
|
1.751937e+00
|
2.126097e+00
|
9.330889e-01
|
2.1345194
|
1.948275
|
3.94710235
|
3.034467
|
1.974070e+00
|
1
|
2.0
|
1
|
2
|
2
|
2
|
2.0
|
2.0
|
1.0
|
4
|
2
|
3.0
|
EqualMeans_12_ASE
|
|
701001810
|
TAAKA VODKA 80 1L
|
701001810
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.54437889
|
0.01000000
|
0.01060199
|
not stationary
|
2.7694231
|
2.9386538
|
3.0241239
|
2.5476282
|
2.2068590
|
1.9775427
|
1.8086172
|
1.6787179
|
1.5862322
|
1.5060897
|
1.4374883
|
1.3850214
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
2.1500000
|
4.5910109
|
4.3387866
|
4.0374311
|
3.4712152
|
3.0265545
|
2.7291639
|
2.5120868
|
2.3031515
|
2.1768817
|
2.0924796
|
1.9910859
|
1.8807362
|
1.9994117
|
2.0113054
|
2.0222596
|
2.0323487
|
2.0416410
|
2.0501993
|
2.0580817
|
2.0653416
|
2.0720280
|
2.0781863
|
2.0838583
|
2.0890822
|
4.163263e+00
|
4.1005963
|
3.8469651
|
3.5846134
|
3.2781221
|
3.0622591
|
2.8507899
|
2.7383287
|
2.5955131
|
2.6034540
|
2.5522387
|
2.4843749
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
1.9763079
|
4.2968421
|
4.2911908
|
4.8025444
|
5.3729971
|
5.2736339
|
5.1200821
|
5.0757839
|
4.9578981
|
4.8594204
|
4.7532709
|
4.6769437
|
4.5169437
|
1.511649e+00
|
1.5856716
|
6.6484741
|
-2.982427e-01
|
1.746964e+00
|
1.785318e+00
|
1.817859e+00
|
1.8454674
|
0.868891
|
1.88876405
|
1.905625
|
1.919930e+00
|
0
|
0.0
|
1
|
2
|
2
|
2
|
2.0
|
2.0
|
1.0
|
2
|
2
|
1.0
|
EqualMeans_12_ASE
|
|
700005866
|
TAAKA VODKA 80 1L
|
700005866
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.31221025
|
0.02148804
|
0.03174915
|
not stationary
|
41.0203241
|
39.3216062
|
37.4481019
|
36.1557088
|
34.5091447
|
32.8639994
|
31.5960751
|
30.2733370
|
28.8379310
|
27.3446318
|
25.8320957
|
24.2926318
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
7.3283333
|
2.0775360
|
2.3394179
|
2.5571922
|
2.9196070
|
3.1780195
|
3.4797540
|
4.2895843
|
5.0141588
|
5.4157855
|
5.5611932
|
5.6039443
|
5.5032397
|
3.1421683
|
4.7655097
|
4.2451054
|
4.9756542
|
4.9146008
|
5.2965478
|
5.3803732
|
5.6156141
|
5.7338443
|
5.8991854
|
6.0148171
|
6.1410207
|
2.214432e+00
|
2.4734601
|
2.8964279
|
3.4077682
|
4.0111561
|
4.7870135
|
6.4359796
|
8.1630524
|
9.6417922
|
11.0790659
|
12.4832663
|
13.8160406
|
2.5152302
|
4.0587504
|
3.0999274
|
3.6955410
|
3.3255503
|
3.5553857
|
3.4126137
|
3.5013026
|
3.4462097
|
3.4804330
|
3.4591738
|
3.4723798
|
9.2666327
|
8.9026054
|
10.6855680
|
13.0907783
|
15.4853964
|
17.6714293
|
20.6486179
|
23.4194441
|
25.6164912
|
27.6086889
|
29.5849732
|
31.2513160
|
6.541185e-01
|
2.0293576
|
3.2816330
|
8.918506e-01
|
1.890484e+00
|
2.598173e+00
|
1.513890e+00
|
0.4059347
|
1.314475
|
1.25664156
|
1.200629
|
5.160187e+00
|
4
|
4.0
|
5
|
4
|
7
|
7
|
12.0
|
5.0
|
4.0
|
7
|
6
|
7.0
|
ARMA_12_ASE
|
|
701001880
|
TAAKA VODKA 80 1L
|
701001880
|
3.857241e-01
|
7.674317e-01
|
white noise
|
white noise
|
0.02966138
|
0.01000000
|
0.01000000
|
inconclusive
|
1.4269017
|
1.4461325
|
1.4610897
|
1.4794658
|
1.4438248
|
1.4059615
|
1.3686600
|
1.3381197
|
1.3157906
|
1.2974145
|
1.2604681
|
1.1828846
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.5174396
|
1.4948883
|
1.4825224
|
1.5006991
|
1.4644323
|
1.4215440
|
1.3823838
|
1.3492160
|
1.3240311
|
1.3028449
|
1.2655291
|
1.1871722
|
1.1998213
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
1.3666667
|
2.006564e+00
|
2.3342891
|
2.3004118
|
2.3140128
|
2.2600330
|
2.2419669
|
2.2286639
|
2.2570145
|
2.3453982
|
2.3518083
|
2.3005831
|
2.2235996
|
2.2651424
|
2.8990216
|
2.4129341
|
2.0937681
|
2.4508967
|
2.4996365
|
2.3321244
|
2.3382486
|
2.4198043
|
2.3981070
|
2.3645226
|
2.3816840
|
3.1199556
|
3.1503628
|
2.7148049
|
2.6544759
|
2.6625206
|
2.6213930
|
2.4272105
|
2.3303058
|
2.2490123
|
2.1530840
|
2.0921738
|
2.0336181
|
6.240092e-01
|
1.3893508
|
0.4234414
|
1.629559e+00
|
-6.083827e-01
|
-5.406915e-01
|
9.984264e-01
|
0.1504591
|
1.624386
|
2.57950379
|
3.559803
|
8.524046e-01
|
2
|
2.0
|
2
|
2
|
1
|
1
|
2.0
|
1.0
|
1.0
|
2
|
1
|
2.0
|
EqualMeans_12_ASE
|
|
700005861
|
TAAKA VODKA 80 1L
|
700005861
|
2.798171e-05
|
8.768368e-04
|
not white noise
|
NA
|
0.11665129
|
0.01000000
|
0.04796742
|
not stationary
|
3.1449053
|
3.4033669
|
3.4895207
|
3.0920848
|
2.8322387
|
2.6534524
|
2.5257478
|
2.4283669
|
2.3526261
|
2.2995464
|
2.2598471
|
2.2449908
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
1.2783333
|
2.9120469
|
3.2251489
|
3.3751316
|
3.1821920
|
2.9660736
|
2.8280312
|
2.6700627
|
2.5279470
|
2.3702667
|
2.2522066
|
2.1634190
|
2.1149156
|
1.9633104
|
1.8684188
|
1.7866728
|
1.7162512
|
1.6555854
|
1.6033237
|
1.5583020
|
1.5195172
|
1.4861054
|
1.4573222
|
1.4325264
|
1.4111657
|
2.962186e+00
|
3.4010894
|
3.5663049
|
3.4222635
|
3.2474177
|
3.0839616
|
2.9365710
|
2.7409038
|
2.5170765
|
2.3283588
|
2.1689325
|
2.0708163
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
2.0995448
|
6.0699551
|
5.8170034
|
5.8041211
|
5.1472329
|
4.7108524
|
4.4383617
|
4.1424992
|
3.8847447
|
3.6384809
|
3.4857644
|
3.3418270
|
3.2749200
|
2.156258e+00
|
3.1286190
|
6.1058690
|
8.714302e-02
|
2.071729e+00
|
1.059042e+00
|
1.048599e+00
|
1.0400025
|
1.032927
|
1.02710284
|
2.022309
|
4.018363e+00
|
3
|
4.0
|
3
|
2
|
1
|
2
|
1.0
|
1.0
|
1.0
|
2
|
1
|
3.0
|
ARIMA_12_ASE
|
|
701001850
|
TAAKA VODKA 80 1L
|
701001850
|
3.111778e-09
|
1.111885e-09
|
not white noise
|
NA
|
0.01000000
|
0.01000000
|
0.01000000
|
inconclusive
|
1.6864744
|
1.6531410
|
1.6420299
|
1.6210897
|
1.6695513
|
1.6646795
|
1.6198077
|
1.6082692
|
1.5995798
|
1.5944231
|
1.5687587
|
1.5544231
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
1.7833333
|
0.9471051
|
0.9410728
|
0.9583839
|
0.9320713
|
1.0150731
|
1.0192416
|
0.9862771
|
0.9582185
|
0.9320831
|
0.9355995
|
0.9036402
|
0.8910035
|
0.6945360
|
1.3803241
|
0.8504484
|
1.1642663
|
1.0882857
|
1.0855645
|
1.1544780
|
1.1234983
|
1.1647393
|
1.1702968
|
1.1837013
|
1.2018058
|
1.231704e+00
|
1.3687870
|
1.4382336
|
1.2802140
|
1.2448374
|
1.1547863
|
1.0938548
|
1.0693608
|
1.0896346
|
1.0644152
|
1.0024592
|
0.9485882
|
0.7516630
|
1.2945436
|
0.6991328
|
1.2715343
|
1.0815054
|
0.8580775
|
1.1862125
|
0.9383167
|
1.0657447
|
1.0882255
|
0.9535772
|
1.0939614
|
1.8266180
|
1.9893472
|
1.8271548
|
2.1182253
|
1.9236300
|
1.8966680
|
1.7713696
|
1.7231753
|
1.6788265
|
1.6423503
|
1.6187286
|
1.5899860
|
1.649683e+00
|
0.4027416
|
0.2461327
|
2.052215e+00
|
-1.627674e-01
|
1.252866e-01
|
9.607176e-01
|
0.9776946
|
1.038822
|
-0.02528883
|
2.005146
|
1.007060e+00
|
2
|
2.0
|
1
|
0
|
0
|
1
|
2.0
|
0.0
|
1.0
|
1
|
1
|
1.0
|
ARMA_12_ASE
|
|
701001901
|
TAAKA VODKA 80 1L
|
701001901
|
6.819545e-12
|
7.586510e-10
|
not white noise
|
NA
|
0.18541887
|
0.01000000
|
0.01000000
|
not stationary
|
0.7879701
|
1.0213034
|
1.1033547
|
1.1533547
|
1.1454060
|
1.1285684
|
1.1388858
|
1.1482265
|
1.1731553
|
1.1741239
|
1.1772475
|
1.1804915
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
0.7687278
|
0.8821437
|
0.9522771
|
0.9727396
|
0.9590509
|
0.9318883
|
0.9316880
|
0.9346157
|
0.9576802
|
0.9540655
|
0.9598836
|
0.9652515
|
1.8236440
|
1.7776671
|
1.7389717
|
1.7064044
|
1.6789949
|
1.6559263
|
1.6365111
|
1.6201707
|
1.6064181
|
1.5948436
|
1.5851021
|
1.5769034
|
7.399023e-01
|
0.8315413
|
0.8883643
|
0.8961124
|
0.8765321
|
0.8336705
|
0.8133006
|
0.7998427
|
0.8038369
|
0.7852240
|
0.7804549
|
0.7791951
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
1.9453676
|
0.6230959
|
0.8141515
|
0.8558566
|
0.9235350
|
0.9969168
|
1.0146397
|
1.0315406
|
1.0370816
|
1.0421818
|
1.0407774
|
1.0285619
|
1.0280019
|
6.237002e-01
|
1.8583984
|
0.9467154
|
2.979949e+00
|
-7.545188e-03
|
1.997161e+00
|
1.998932e+00
|
1.9995980
|
2.999849
|
1.99994307
|
1.999979
|
1.999992e+00
|
1
|
4.0
|
2
|
2
|
2
|
2
|
3.0
|
2.0
|
3.0
|
2
|
2
|
1.0
|
ARIMA_12_ASE
|
|
701001904
|
TAAKA VODKA 80 1L
|
701001904
|
9.048454e-08
|
5.283449e-07
|
not white noise
|
NA
|
0.43473483
|
0.01000000
|
0.01000000
|
not stationary
|
1.1955769
|
1.2096795
|
1.2229274
|
1.2218590
|
1.2048077
|
1.1964316
|
1.1941117
|
1.1949359
|
1.1944373
|
1.1976282
|
1.2035023
|
1.2069017
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4666667
|
1.4841602
|
1.5389074
|
1.4902713
|
1.4144033
|
1.3738584
|
1.3599818
|
1.3405575
|
1.3257438
|
1.3175436
|
1.3013227
|
1.3052061
|
1.3102041
|
1.5810320
|
2.0646698
|
2.6573867
|
1.9097428
|
1.9640995
|
1.9062874
|
1.9416191
|
1.7303295
|
1.7743717
|
1.7186676
|
1.6788633
|
1.6447738
|
1.282673e+00
|
1.5206545
|
1.4229317
|
1.3809908
|
1.4082162
|
1.4251940
|
1.4167668
|
1.4330891
|
1.4325368
|
1.4267040
|
1.4439083
|
1.4253409
|
1.8423873
|
2.4863038
|
3.2129767
|
2.5352098
|
2.6448515
|
2.6991264
|
2.8165294
|
2.6127613
|
2.7216841
|
2.7222384
|
2.6995275
|
2.6989619
|
3.0800760
|
2.3915354
|
2.3939485
|
2.4090143
|
2.4395653
|
2.3694690
|
2.3644406
|
2.3630603
|
2.3654867
|
2.3960114
|
2.4235625
|
2.3890228
|
1.143407e+00
|
2.2319445
|
4.0112431
|
1.447822e-01
|
1.509765e+00
|
1.494126e+00
|
1.106289e+00
|
2.2140312
|
1.235630
|
1.06777297
|
3.091769
|
4.110406e+00
|
2
|
2.0
|
2
|
3
|
1
|
2
|
2.0
|
1.0
|
2.0
|
2
|
2
|
3.0
|
EqualMeans_12_ASE
|
|
701000321
|
TAAKA VODKA 80 1L
|
701000321
|
9.021729e-04
|
1.759268e-02
|
not white noise
|
NA
|
0.01899300
|
0.01000000
|
0.10000000
|
stationary
|
1.1710043
|
1.2004915
|
1.2094658
|
1.0908761
|
0.9925427
|
0.9056197
|
0.8398687
|
0.7668376
|
0.7063319
|
0.6671581
|
0.6332420
|
0.6049786
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
1.2166667
|
4.9505043
|
2.9344969
|
2.3457840
|
2.5624829
|
2.4150441
|
2.2656664
|
2.1414018
|
1.9553184
|
1.8213283
|
1.7053062
|
1.6001769
|
1.4984614
|
1.5723548
|
1.4633890
|
1.5054031
|
1.2272708
|
1.0921195
|
1.0500487
|
1.0002845
|
1.0361209
|
1.1136744
|
1.1867192
|
1.2578197
|
1.3051289
|
1.322547e+00
|
1.7211078
|
1.8703293
|
1.9101641
|
1.8088129
|
1.6926424
|
1.6207221
|
1.5910010
|
1.5188421
|
1.4191297
|
1.3462616
|
1.3141439
|
1.2204117
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
1.0000166
|
0.8263359
|
0.7231268
|
0.9128621
|
1.1243435
|
1.1876421
|
1.1582706
|
1.1786536
|
1.1696388
|
1.1729235
|
1.1849318
|
1.2007783
|
1.2167080
|
2.000000e+00
|
2.0000000
|
4.0000000
|
-2.405483e-17
|
-1.350771e-16
|
1.000000e+00
|
-9.344377e-17
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
2.000000e+00
|
2
|
2.0
|
1
|
1
|
2
|
2
|
1.0
|
1.0
|
1.0
|
0
|
1
|
1.0
|
EqualMeans_12_ASE
|
|
701001902
|
TAAKA VODKA 80 1L
|
701001902
|
0.000000e+00
|
1.110223e-16
|
not white noise
|
NA
|
0.36038955
|
0.01000000
|
0.01000000
|
not stationary
|
1.2988034
|
1.3129060
|
1.3201709
|
1.4180342
|
1.4875214
|
1.5586325
|
1.7266422
|
1.8734829
|
1.9894017
|
2.0936752
|
2.1824864
|
2.2449573
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.8000000
|
1.3417939
|
1.3484977
|
1.3506908
|
1.3635321
|
1.4317118
|
1.5017399
|
1.6695923
|
1.8108711
|
1.9283069
|
2.0358965
|
2.1287877
|
2.1928990
|
3.7100420
|
2.9400814
|
2.4805010
|
2.2061830
|
2.0424458
|
1.9447130
|
1.8863775
|
1.8515577
|
1.8307742
|
1.8183687
|
1.8109641
|
1.8065443
|
1.367044e+00
|
1.4393103
|
1.4454570
|
1.2712999
|
1.3515299
|
1.4066517
|
1.4658585
|
1.5148079
|
1.6072195
|
1.7576524
|
1.8606205
|
1.9186453
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
3.9779756
|
2.1767938
|
1.9467462
|
1.9045347
|
1.7544638
|
1.7551391
|
1.7862844
|
1.8603817
|
1.9505741
|
1.9893415
|
2.0347064
|
2.0739335
|
2.1491457
|
2.624291e+00
|
3.4435520
|
3.0438653
|
2.816562e+00
|
1.619171e+00
|
2.475240e+00
|
2.363029e+00
|
3.2778318
|
1.212473
|
2.16253670
|
3.124322
|
5.095097e+00
|
2
|
2.0
|
2
|
4
|
2
|
3
|
5.0
|
3.0
|
3.0
|
1
|
2
|
2.0
|
ARIMA_12_ASE
|
|
700004770
|
TAAKA VODKA 80 1L
|
700004770
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.67246321
|
0.01000000
|
0.01000000
|
not stationary
|
2.9373276
|
2.9334814
|
2.4566438
|
2.3000840
|
2.2445583
|
2.2117293
|
2.0643972
|
1.9509494
|
1.8610028
|
1.8277122
|
1.8067681
|
1.7906182
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
3.3316667
|
2.7372199
|
2.7608743
|
2.6294740
|
2.5938914
|
2.5092408
|
2.5661585
|
2.6474384
|
2.7557645
|
2.9071031
|
3.0625002
|
3.0725270
|
3.1218363
|
2.9293446
|
2.9539608
|
2.9770709
|
2.9987670
|
3.0191356
|
3.0382579
|
3.0562102
|
3.0730642
|
3.0888869
|
3.1037414
|
3.1176871
|
3.1307795
|
3.295872e+00
|
2.7258590
|
2.6967588
|
2.7422302
|
2.7448767
|
2.7308850
|
2.8594171
|
3.0683988
|
3.2781511
|
3.5145773
|
3.5802386
|
3.7311803
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
2.9725420
|
15.3958816
|
15.8869049
|
15.9214910
|
15.3606947
|
15.1356711
|
14.9409582
|
14.6203682
|
14.6236395
|
14.4563891
|
14.2016280
|
13.3333187
|
12.5893127
|
1.306533e+00
|
4.7272362
|
2.0822285
|
1.381773e+00
|
2.634530e+00
|
4.847809e+00
|
3.027774e+00
|
2.1796301
|
2.307767
|
1.41588971
|
3.507124
|
1.584109e+00
|
4
|
4.0
|
3
|
1
|
2
|
2
|
4.0
|
2.0
|
3.0
|
1
|
2
|
5.0
|
EqualMeans_12_ASE
|
|
701000320
|
TAAKA VODKA 80 1L
|
701000320
|
7.397740e-02
|
1.020833e-01
|
white noise
|
white noise
|
0.02456406
|
0.01000000
|
0.10000000
|
stationary
|
0.3058120
|
0.2891453
|
0.2835897
|
0.2808120
|
0.2801709
|
0.2660684
|
0.2607570
|
0.2574145
|
0.2610826
|
0.2742735
|
0.2852991
|
0.2949145
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
0.4051081
|
0.3400124
|
0.3178584
|
0.3010784
|
0.3005917
|
0.2830615
|
0.2753256
|
0.2701542
|
0.2723935
|
0.2844376
|
0.2945231
|
0.3040786
|
1.1262931
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
1.1500000
|
3.367507e-01
|
0.3334983
|
0.3298248
|
0.3271361
|
0.3369339
|
0.3258995
|
0.3179715
|
0.3165767
|
0.3249886
|
0.3380303
|
0.3485719
|
0.3592423
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0000000
|
1.0951482
|
1.0391884
|
1.0373243
|
0.9956590
|
0.9750075
|
0.9688032
|
0.9617803
|
0.9495702
|
0.9384780
|
0.9410152
|
0.9327267
|
0.9238507
|
-2.835440e-01
|
1.0000000
|
1.0000000
|
1.000000e+00
|
0.000000e+00
|
2.000000e+00
|
1.000000e+00
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
1.000000e+00
|
2
|
2.0
|
1
|
1
|
1
|
1
|
0.0
|
1.0
|
2.0
|
0
|
1
|
1.0
|
EqualMeans_12_ASE
|
|
700005926
|
TAAKA VODKA 80 1L
|
700005926
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.02043425
|
0.01000000
|
0.01000000
|
inconclusive
|
1.2773077
|
1.2785897
|
1.2738889
|
1.2734615
|
1.2670513
|
1.2888462
|
1.3058791
|
1.3186538
|
1.3055128
|
1.2732051
|
1.2467716
|
1.2067949
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.5333333
|
1.4330655
|
1.5153141
|
1.4548217
|
1.5009097
|
1.3966455
|
1.3343924
|
1.2841664
|
1.2087831
|
1.1483447
|
1.0884805
|
1.0262071
|
0.9793421
|
2.2002900
|
2.1617869
|
2.1255065
|
2.0913205
|
2.0591081
|
2.0287553
|
2.0001548
|
1.9732053
|
1.9478117
|
1.9238840
|
1.9013376
|
1.8800928
|
2.667315e+00
|
3.4118599
|
3.8792223
|
3.8945939
|
4.0508092
|
3.9659928
|
3.9156449
|
3.7875039
|
3.7042064
|
3.5755196
|
3.5150700
|
3.4031691
|
1.6954044
|
1.9238215
|
2.0300241
|
1.9299860
|
2.3085755
|
2.3687749
|
2.2688382
|
2.2545610
|
2.2051669
|
2.0706909
|
2.0700251
|
2.1066515
|
9.7833663
|
7.4145723
|
5.6429707
|
4.8216838
|
4.4154853
|
4.1634533
|
3.9056714
|
3.6334260
|
3.4048642
|
3.2365527
|
3.0866317
|
2.9483866
|
1.683491e+00
|
0.0158938
|
2.4608878
|
5.416634e-01
|
2.256431e+00
|
1.826491e+00
|
1.551416e+00
|
2.6000376
|
2.910352
|
2.25189453
|
3.394532
|
2.261570e+00
|
3
|
2.0
|
1
|
2
|
1
|
3
|
1.0
|
2.0
|
2.0
|
2
|
2
|
2.0
|
ARMA_12_ASE
|
|
701001800
|
TAAKA VODKA 80 1L
|
701001800
|
1.224362e-01
|
4.792812e-01
|
white noise
|
white noise
|
0.21045181
|
0.01000000
|
0.03920430
|
not stationary
|
0.3269444
|
0.3769444
|
0.3799359
|
0.4064316
|
0.4248932
|
0.4372009
|
0.4547833
|
0.4801496
|
0.4956054
|
0.5087393
|
0.5194852
|
0.5196795
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.3269444
|
0.3769444
|
0.3799359
|
0.4064316
|
0.4248932
|
0.4372009
|
0.4547833
|
0.4801496
|
0.4956054
|
0.5087393
|
0.5194852
|
0.5196795
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
0.8333333
|
3.715443e-01
|
0.4106859
|
0.4270984
|
0.4572844
|
0.4870052
|
0.5077215
|
0.5265536
|
0.5514860
|
0.5634607
|
0.5780082
|
0.5876334
|
0.5895793
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
0.8826079
|
1.7195772
|
1.7795639
|
1.6700771
|
1.6177478
|
1.5964515
|
1.5903798
|
1.5545462
|
1.5487558
|
1.5511204
|
1.5446621
|
1.5448047
|
1.5176862
|
8.789558e-01
|
-0.2864555
|
1.7378374
|
3.338108e-01
|
1.039229e+00
|
-1.243127e-01
|
1.210245e+00
|
0.2435835
|
1.099384
|
0.97692315
|
2.054678
|
1.121891e+00
|
1
|
2.0
|
1
|
2
|
0
|
1
|
2.0
|
2.0
|
1.0
|
1
|
1
|
1.0
|
EqualMeans_12_ASE
|
|
700003059
|
TAAKA VODKA 80 1L
|
700003059
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.01000000
|
0.01000000
|
0.01000000
|
inconclusive
|
1.3594872
|
1.3876923
|
1.3475214
|
1.3319231
|
1.3246154
|
1.2406838
|
1.1990476
|
1.1681410
|
1.1401140
|
1.1015385
|
1.0662471
|
1.0265812
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7333333
|
0.7822079
|
0.8905432
|
0.8879031
|
0.9104011
|
0.9222210
|
0.8704409
|
0.8534507
|
0.8295435
|
0.8112416
|
0.7765995
|
0.7437519
|
0.7113119
|
0.9216603
|
1.5009765
|
0.9665898
|
1.4078036
|
0.9958728
|
1.3314352
|
1.0134655
|
1.2682290
|
1.0223817
|
1.2153779
|
1.0249141
|
1.1707156
|
2.591532e+00
|
2.7108124
|
2.4617483
|
2.3576535
|
2.2807949
|
2.1356822
|
2.1222404
|
2.1004201
|
2.0960634
|
2.0576792
|
2.0084829
|
1.9710193
|
1.2748582
|
1.3430106
|
0.7381308
|
1.0936850
|
1.1334287
|
0.9515117
|
1.0499681
|
1.0673239
|
1.0129014
|
1.0399485
|
1.0466752
|
1.0304761
|
1.7414562
|
1.8158592
|
1.7079300
|
1.6763549
|
1.7339938
|
1.7245794
|
1.7257332
|
1.7230273
|
1.6985303
|
1.6307441
|
1.5755449
|
1.5202460
|
-2.765066e-01
|
1.3644443
|
0.4884796
|
9.041992e-01
|
3.579109e+00
|
1.527870e+00
|
1.258682e+00
|
2.1668142
|
1.913767
|
1.70682274
|
1.791777
|
-9.828707e-02
|
1
|
0.0
|
1
|
1
|
1
|
1
|
2.0
|
1.0
|
2.0
|
1
|
2
|
0.0
|
ARMA_12_ASE
|
|
701000360
|
TAAKA VODKA 80 1L
|
701000360
|
0.000000e+00
|
0.000000e+00
|
not white noise
|
NA
|
0.01782149
|
0.01000000
|
0.01000000
|
inconclusive
|
1.0547703
|
1.1156677
|
1.1445139
|
1.1615011
|
1.1998985
|
1.2030609
|
1.2641110
|
1.2986806
|
1.3393857
|
1.4147703
|
1.4755162
|
1.5616079
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7083333
|
0.7409955
|
0.6622150
|
0.7679869
|
0.7996367
|
0.8200111
|
0.8356037
|
0.8565329
|
0.9073472
|
0.9353860
|
1.0023231
|
1.0571398
|
1.1326307
|
1.2880147
|
1.9607699
|
1.5563160
|
1.5176694
|
1.4476984
|
1.3413498
|
1.2904318
|
1.2191043
|
1.1667074
|
1.1166898
|
1.0722279
|
1.0332015
|
6.671504e-01
|
0.6490383
|
0.7694516
|
0.8198088
|
0.8097014
|
0.8220716
|
0.8006063
|
0.8420725
|
0.8396961
|
0.8555306
|
0.8745281
|
0.9032365
|
1.5484043
|
2.2453039
|
2.0482803
|
2.0237832
|
2.1001411
|
2.0408910
|
2.0708135
|
2.0614928
|
2.0611865
|
2.0640606
|
2.0616581
|
2.0629336
|
0.8000180
|
0.7884722
|
0.8333405
|
0.8557746
|
0.8692351
|
0.8782087
|
0.8846184
|
0.8894258
|
0.8931648
|
0.8961560
|
0.8986033
|
0.9006428
|
1.280019e+00
|
1.6965226
|
1.0000000
|
0.000000e+00
|
2.000000e+00
|
0.000000e+00
|
2.000000e+00
|
1.0000000
|
1.000000
|
2.00000000
|
1.000000
|
3.000000e+00
|
1
|
2.0
|
1
|
1
|
2
|
1
|
3.0
|
0.0
|
2.0
|
3
|
2
|
3.0
|
ARIMA_S12_12_ASE
|
|
700004711
|
TAAKA VODKA 80 1L
|
700004711
|
1.225766e-01
|
5.601297e-01
|
white noise
|
white noise
|
0.34105678
|
0.01000000
|
0.01000000
|
not stationary
|
0.2550041
|
0.2575682
|
0.2569699
|
0.2547476
|
0.2537733
|
0.2535511
|
0.2547476
|
0.2746515
|
0.3067420
|
0.3466964
|
0.3926031
|
0.4436152
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.2550041
|
0.2575682
|
0.2569699
|
0.2547476
|
0.2537733
|
0.2535511
|
0.2547476
|
0.2746515
|
0.3067420
|
0.3466964
|
0.3926031
|
0.4436152
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
0.5166667
|
3.361097e-01
|
0.3155127
|
0.2919478
|
0.2764727
|
0.2586763
|
0.2496937
|
0.2544425
|
0.2763915
|
0.3005871
|
0.3288234
|
0.3599117
|
0.3946049
|
0.8174024
|
0.6424489
|
0.6798952
|
0.6718803
|
0.6735958
|
0.6732286
|
0.6733072
|
0.6732904
|
0.6732940
|
0.6732932
|
0.6732934
|
0.6732934
|
0.5959641
|
0.5948793
|
0.4870828
|
0.4477523
|
0.4126828
|
0.4140054
|
0.4148010
|
0.4167965
|
0.4173774
|
0.4165885
|
0.4219115
|
0.4476560
|
5.736152e-17
|
1.0000000
|
1.0000000
|
5.736152e-17
|
1.000000e+00
|
7.817820e-17
|
1.000000e+00
|
1.0000000
|
1.000000
|
1.00000000
|
1.000000
|
1.128727e-16
|
0
|
1.0
|
1
|
1
|
1
|
0
|
0.0
|
2.0
|
2.0
|
2
|
2
|
2.0
|
ARIMA_12_ASE
|
aggregated_forecast = results[c("Product_Type","Product","Customer","winning_12","ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
for (i in 1:z){
if (results$winning_12[i] == "EqualMeans_12_ASE"){
aggregated_forecast$F1[i] <- results$EqualMeans_F1[i]
aggregated_forecast$F2[i] <- results$EqualMeans_F2[i]
aggregated_forecast$F3[i] <- results$EqualMeans_F3[i]
aggregated_forecast$F4[i] <- results$EqualMeans_F4[i]
aggregated_forecast$F5[i] <- results$EqualMeans_F5[i]
aggregated_forecast$F6[i] <- results$EqualMeans_F6[i]
aggregated_forecast$F7[i] <- results$EqualMeans_F7[i]
aggregated_forecast$F8[i] <- results$EqualMeans_F8[i]
aggregated_forecast$F9[i] <- results$EqualMeans_F9[i]
aggregated_forecast$F10[i] <- results$EqualMeans_F10[i]
aggregated_forecast$F11[i] <- results$EqualMeans_F11[i]
aggregated_forecast$F12[i] <- results$EqualMeans_F12[i]
} else if (results$winning_12[i] == "ARMA_12_ASE"){
aggregated_forecast$F1[i] <- results$ARMA_F1[i]
aggregated_forecast$F2[i] <- results$ARMA_F2[i]
aggregated_forecast$F3[i] <- results$ARMA_F3[i]
aggregated_forecast$F4[i] <- results$ARMA_F4[i]
aggregated_forecast$F5[i] <- results$ARMA_F5[i]
aggregated_forecast$F6[i] <- results$ARMA_F6[i]
aggregated_forecast$F7[i] <- results$ARMA_F7[i]
aggregated_forecast$F8[i] <- results$ARMA_F8[i]
aggregated_forecast$F9[i] <- results$ARMA_F9[i]
aggregated_forecast$F10[i] <- results$ARMA_F10[i]
aggregated_forecast$F11[i] <- results$ARMA_F11[i]
aggregated_forecast$F12[i] <- results$ARMA_F12[i]
} else if (results$winning_12[i] == "ARIMA_12_ASE"){
aggregated_forecast$F1[i] <- results$ARIMA_F1[i]
aggregated_forecast$F2[i] <- results$ARIMA_F2[i]
aggregated_forecast$F3[i] <- results$ARIMA_F3[i]
aggregated_forecast$F4[i] <- results$ARIMA_F4[i]
aggregated_forecast$F5[i] <- results$ARIMA_F5[i]
aggregated_forecast$F6[i] <- results$ARIMA_F6[i]
aggregated_forecast$F7[i] <- results$ARIMA_F7[i]
aggregated_forecast$F8[i] <- results$ARIMA_F8[i]
aggregated_forecast$F9[i] <- results$ARIMA_F9[i]
aggregated_forecast$F10[i] <- results$ARIMA_F10[i]
aggregated_forecast$F11[i] <- results$ARIMA_F11[i]
aggregated_forecast$F12[i] <- results$ARIMA_F12[i]
} else if (results$winning_12[i] == "ARIMA_S12_12_ASE"){
aggregated_forecast$F1[i] <- results$ARIMA_S12_F1[i]
aggregated_forecast$F2[i] <- results$ARIMA_S12_F2[i]
aggregated_forecast$F3[i] <- results$ARIMA_S12_F3[i]
aggregated_forecast$F4[i] <- results$ARIMA_S12_F4[i]
aggregated_forecast$F5[i] <- results$ARIMA_S12_F5[i]
aggregated_forecast$F6[i] <- results$ARIMA_S12_F6[i]
aggregated_forecast$F7[i] <- results$ARIMA_S12_F7[i]
aggregated_forecast$F8[i] <- results$ARIMA_S12_F8[i]
aggregated_forecast$F9[i] <- results$ARIMA_S12_F9[i]
aggregated_forecast$F10[i] <- results$ARIMA_S12_F10[i]
aggregated_forecast$F11[i] <- results$ARIMA_S12_F11[i]
aggregated_forecast$F12[i] <- results$ARIMA_S12_F12[i]
}
}
# other time series that did not have enough data points, will use mean for forecasts
zz = nrow(combinations_mean)
results_mean <- data.frame(Product_Type=integer(),
Product=character(),
Customer=integer(),
EqualMeans_F1=double(),
EqualMeans_F2=double(),
EqualMeans_F3=double(),
EqualMeans_F4=double(),
EqualMeans_F5=double(),
EqualMeans_F6=double(),
EqualMeans_F7=double(),
EqualMeans_F8=double(),
EqualMeans_F9=double(),
EqualMeans_F10=double(),
EqualMeans_F11=double(),
EqualMeans_F12=double(),
ACTUAL_1=double(),
ACTUAL_2=double(),
ACTUAL_3=double(),
ACTUAL_4=double(),
ACTUAL_5=double(),
ACTUAL_6=double(),
ACTUAL_7=double(),
ACTUAL_8=double(),
ACTUAL_9=double(),
ACTUAL_10=double(),
ACTUAL_11=double(),
ACTUAL_12=double(),
stringsAsFactors = FALSE)
# loop through sample combinations
for(i in 1:zz) {
sample_combinations1 = combinations_mean[i,]
temp1 = inner_join(temp_mean,sample_combinations1)
product = sample_combinations1$Product
customer = sample_combinations1$Customer_ID
product_type = sample_combinations1$Customer_ID
results_mean[i,"Product_Type"] = product_type
results_mean[i,"Product"] = as.character(sample_combinations1$Product)
results_mean[i,"Customer"] = customer
par(mfrow=c(1,1))
plot.ts(temp1$STD_Cases,
main=c(paste("Standard Case Sales of ", product),
paste("for Customer",customer)),
xlab="Months",
ylab="Standard Cases")
j=12
#Equal Means Model
trainingSize = 60
ASEHolder1 = numeric()
ASEHolder2 = numeric()
ASEHolder3 = numeric()
ASEHolder4 = numeric()
ASEHolder5 = numeric()
ASEHolder6 = numeric()
ASEHolder7 = numeric()
ASEHolder8 = numeric()
ASEHolder9 = numeric()
ASEHolder10 = numeric()
ASEHolder11 = numeric()
ASEHolder12 = numeric()
for( k in 1:(84-(trainingSize + j) + 1))
{
sink("file")
model0_mean = mean(temp1$STD_Cases[k:(k+(trainingSize-1))])
ASEHolder1[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 1 - 1)] - model0_mean)^2)
ASEHolder2[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 2 - 1)] - model0_mean)^2)
ASEHolder3[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 3 - 1)] - model0_mean)^2)
ASEHolder4[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 4 - 1)] - model0_mean)^2)
ASEHolder5[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 5 - 1)] - model0_mean)^2)
ASEHolder6[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 6 - 1)] - model0_mean)^2)
ASEHolder7[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 7 - 1)] - model0_mean)^2)
ASEHolder8[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 8 - 1)] - model0_mean)^2)
ASEHolder9[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 9 - 1)] - model0_mean)^2)
ASEHolder10[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 10 - 1)] - model0_mean)^2)
ASEHolder11[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 11 - 1)] - model0_mean)^2)
ASEHolder12[k] = mean((temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + 12 - 1)] - model0_mean)^2)
sink()
assign(paste("EqualMeans_Results_",k,sep=""),(temp1$STD_Cases[(trainingSize+k):(trainingSize+ k + j - 1)] - model0_mean)^2)
}
WindowedASE1 = mean(ASEHolder1)
WindowedASE2 = mean(ASEHolder2)
WindowedASE3 = mean(ASEHolder3)
WindowedASE4 = mean(ASEHolder4)
WindowedASE5 = mean(ASEHolder5)
WindowedASE6 = mean(ASEHolder6)
WindowedASE7 = mean(ASEHolder7)
WindowedASE8 = mean(ASEHolder8)
WindowedASE9 = mean(ASEHolder9)
WindowedASE10 = mean(ASEHolder10)
WindowedASE11 = mean(ASEHolder11)
WindowedASE12 = mean(ASEHolder12)
results_mean[i,paste0("EqualMeans_1_ASE")] = WindowedASE1
results_mean[i,paste0("EqualMeans_2_ASE")] = WindowedASE2
results_mean[i,paste0("EqualMeans_3_ASE")] = WindowedASE3
results_mean[i,paste0("EqualMeans_4_ASE")] = WindowedASE4
results_mean[i,paste0("EqualMeans_5_ASE")] = WindowedASE5
results_mean[i,paste0("EqualMeans_6_ASE")] = WindowedASE6
results_mean[i,paste0("EqualMeans_7_ASE")] = WindowedASE7
results_mean[i,paste0("EqualMeans_8_ASE")] = WindowedASE8
results_mean[i,paste0("EqualMeans_9_ASE")] = WindowedASE9
results_mean[i,paste0("EqualMeans_10_ASE")] = WindowedASE10
results_mean[i,paste0("EqualMeans_11_ASE")] = WindowedASE11
results_mean[i,paste0("EqualMeans_12_ASE")] = WindowedASE12
results_mean[i,paste0("EqualMeans_F1")] = model0_mean
results_mean[i,paste0("EqualMeans_F2")] = model0_mean
results_mean[i,paste0("EqualMeans_F3")] = model0_mean
results_mean[i,paste0("EqualMeans_F4")] = model0_mean
results_mean[i,paste0("EqualMeans_F5")] = model0_mean
results_mean[i,paste0("EqualMeans_F6")] = model0_mean
results_mean[i,paste0("EqualMeans_F7")] = model0_mean
results_mean[i,paste0("EqualMeans_F8")] = model0_mean
results_mean[i,paste0("EqualMeans_F9")] = model0_mean
results_mean[i,paste0("EqualMeans_F10")] = model0_mean
results_mean[i,paste0("EqualMeans_F11")] = model0_mean
results_mean[i,paste0("EqualMeans_F12")] = model0_mean
results_mean[i,paste0("ACTUAL_1")] = temp1$STD_Cases[73]
results_mean[i,paste0("ACTUAL_2")] = temp1$STD_Cases[74]
results_mean[i,paste0("ACTUAL_3")] = temp1$STD_Cases[75]
results_mean[i,paste0("ACTUAL_4")] = temp1$STD_Cases[76]
results_mean[i,paste0("ACTUAL_5")] = temp1$STD_Cases[77]
results_mean[i,paste0("ACTUAL_6")] = temp1$STD_Cases[78]
results_mean[i,paste0("ACTUAL_7")] = temp1$STD_Cases[79]
results_mean[i,paste0("ACTUAL_8")] = temp1$STD_Cases[80]
results_mean[i,paste0("ACTUAL_9")] = temp1$STD_Cases[81]
results_mean[i,paste0("ACTUAL_10")] = temp1$STD_Cases[82]
results_mean[i,paste0("ACTUAL_11")] = temp1$STD_Cases[83]
results_mean[i,paste0("ACTUAL_12")] = temp1$STD_Cases[84]
}




|
Product_Type
|
Product
|
Customer
|
EqualMeans_F1
|
EqualMeans_F2
|
EqualMeans_F3
|
EqualMeans_F4
|
EqualMeans_F5
|
EqualMeans_F6
|
EqualMeans_F7
|
EqualMeans_F8
|
EqualMeans_F9
|
EqualMeans_F10
|
EqualMeans_F11
|
EqualMeans_F12
|
ACTUAL_1
|
ACTUAL_2
|
ACTUAL_3
|
ACTUAL_4
|
ACTUAL_5
|
ACTUAL_6
|
ACTUAL_7
|
ACTUAL_8
|
ACTUAL_9
|
ACTUAL_10
|
ACTUAL_11
|
ACTUAL_12
|
EqualMeans_1_ASE
|
EqualMeans_2_ASE
|
EqualMeans_3_ASE
|
EqualMeans_4_ASE
|
EqualMeans_5_ASE
|
EqualMeans_6_ASE
|
EqualMeans_7_ASE
|
EqualMeans_8_ASE
|
EqualMeans_9_ASE
|
EqualMeans_10_ASE
|
EqualMeans_11_ASE
|
EqualMeans_12_ASE
|
winning_12
|
|
700005895
|
TAAKA VODKA 80 1L
|
700005895
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
0.4166667
|
1
|
2
|
0
|
2
|
2
|
0
|
2
|
2
|
2
|
2
|
2
|
2
|
0.4156197
|
0.4976709
|
0.5207479
|
0.5726709
|
0.6376709
|
0.6788675
|
0.7321032
|
0.7938248
|
0.8606339
|
0.9310043
|
1.0048970
|
1.0839957
|
EqualMeans_12_ASE
|
|
701000357
|
TAAKA VODKA 80 1L
|
701000357
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0.6166667
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0.3264744
|
0.3328846
|
0.3350214
|
0.3367308
|
0.3403205
|
0.3444231
|
0.3473535
|
0.3495513
|
0.3512607
|
0.3526282
|
0.3537471
|
0.3546795
|
EqualMeans_12_ASE
|
|
700005867
|
TAAKA VODKA 80 1L
|
700005867
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
0.2250000
|
1
|
1
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
1
|
0.2278045
|
0.2489583
|
0.2401976
|
0.2358173
|
0.2237019
|
0.2160524
|
0.2173649
|
0.2183494
|
0.2135595
|
0.2099840
|
0.2072917
|
0.2090011
|
EqualMeans_12_ASE
|
|
701001908
|
TAAKA VODKA 80 1L
|
701001908
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
0.0000000
|
EqualMeans_12_ASE
|
aggregated_forecast_mean = results_mean[c("Product_Type","Product","Customer","winning_12","ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
aggregated_forecast_mean$F1 <- results_mean$EqualMeans_F1
aggregated_forecast_mean$F2 <- results_mean$EqualMeans_F2
aggregated_forecast_mean$F3 <- results_mean$EqualMeans_F3
aggregated_forecast_mean$F4 <- results_mean$EqualMeans_F4
aggregated_forecast_mean$F5 <- results_mean$EqualMeans_F5
aggregated_forecast_mean$F6 <- results_mean$EqualMeans_F6
aggregated_forecast_mean$F7 <- results_mean$EqualMeans_F7
aggregated_forecast_mean$F8 <- results_mean$EqualMeans_F8
aggregated_forecast_mean$F9 <- results_mean$EqualMeans_F9
aggregated_forecast_mean$F10 <- results_mean$EqualMeans_F10
aggregated_forecast_mean$F11 <- results_mean$EqualMeans_F11
aggregated_forecast_mean$F12 <- results_mean$EqualMeans_F12
combined_aggregated_forecast <- rbind(aggregated_forecast, aggregated_forecast_mean)
combined_actuals_forecasts <- combined_aggregated_forecast %>% summarize_if(is.numeric,sum,na.rm=TRUE)
actuals <- combined_actuals_forecasts[c("ACTUAL_1","ACTUAL_2","ACTUAL_3","ACTUAL_4","ACTUAL_5","ACTUAL_6","ACTUAL_7","ACTUAL_8","ACTUAL_9","ACTUAL_10","ACTUAL_11","ACTUAL_12")]
actuals_trans <- transpose(actuals)
forecasts <- combined_actuals_forecasts[c("F1","F2","F3","F4","F5","F6","F7","F8","F9","F10","F11","F12")]
forecasts_trans <- transpose(forecasts)